Internet Finance and Digital Economy

The following sections are included:

• Preface
• About the Editor
• Contents

China has long adopted the approval system for stock issuance, and the threshold for A-share listing is high, so many companies cannot meet the strict listing requirements for A-shares, while the United States has a developed and open capital market, which has attracted many Chinese companies to list in the United States, forming a group of Chinese stocks listed in the United States. This paper studies the valuation and financing constraint effects of US-listed Chinese stocks and A-share listed companies. The results conclude that the valuation of US-listed Chinese stocks is lower than that of A-share listed companies compared with similar companies listed in A-share, and the financing constraints of US-listed Chinese stocks have not been eased. This paper enriches the research related to overseas listing and provides policy recommendations for overseas-listed Chinese companies and further improvement of the Chinese capital market.

This paper uses the literature about new generation information technology gathered in the CNKI database as the research object, and makes a visual analysis of the number of articles, keywords, and other aspects using CiteSpace literature analysis software to understand the research hotspots and trends of the new generation information technology in China. The research shows that China’s new generation of information technology research has experienced three periods: from the early start stage (1993–2010) to the steady development stage (2011–2016) and then to the high-speed development stage (2017–present). At the moment, research hotspots are primarily focused on theoretical research in artificial intelligence, intelligent manufacturing, and the digital economy, as well as the design and application of new-generation information technology. Research trends: First, research on the innovation path of cross-border integration of new generation information technology. Second, research on the path of the digital economy to an intelligent economy. Third, the research of emerging technologies with artificial intelligence is the core.

With the continuous development of informatization, accounting computerization has become an indispensable and necessary trend for enterprises to carry out financial accounting and management, improve economic benefits and reduce operational risks. Auditing can supervise the production and operation activities of an enterprise, prevent major financial fraud, and maintain property safety. As an objective fact, audit risk has an immeasurable impact on enterprises, and the lack of relevant laws, regulations, and standards involved in the development of cloud accounting has hindered its practical application to a certain extent. This paper takes COBIT as the research object and combines the literature data to construct an index system for financial analysis work in the cloud computing environment under the current development status of the AIS industry in our country. Based on this, a scale-free network evaluation model based on B-A is established to quantify audit risk factors, thereby improving the decision-making efficiency and effect of enterprise management.

Digital finance, supported by advanced technologies such as big data, artificial intelligence, cloud computing, and blockchain, is profoundly changing the financial industry and model and reshaping the financial landscape. The integration and development of digital finance technology with commercial banks has enhanced the technology level of commercial banks and improved their operation and management, thus weakening commercial banks’ risk-taking level. At the same time, the development of digital finance has also prompted commercial banks to use big data technology to establish a risk early warning system and improve their risk warning and prevention capabilities. In this paper, we construct a partial equilibrium model of commercial banks’ risk-taking behavior under the constraints of digital finance and deduce the transmission mechanism of “digital finance — management cost — bank risk-taking”. A fixed-effects model is built to empirically test 163 commercial banks in China from 2011 to 2018. The results show that: (1) digital finance significantly reduces the risk-taking level of commercial banks; (2) digital finance reduces the risk-taking level of commercial banks through the channels of the breadth of coverage, depth of use, degree of payment and settlement support services, degree of credit investigation support services, and degree of digital services; (3) the use of deep channels of digital finance has the most apparent effect on reducing the risk-taking level of commercial banks.

In the downward phase of the economic cycle, there is an abnormal phenomenon that the sales of luxury goods increase instead of decrease, which is generally believed to be caused by the influence strategy of luxury goods. However, this view—luxury sales are less affected by the economic cycle and should maintain a slow growth trend year by year, cannot explain the reason why luxury sales show a significant growth trend in the downward phase of the economic cycle. In order to explain this phenomenon, this paper puts forward the alternative theory, which believes that people’s consumption behavior is inevitable, but there can be differences in consumer goods. When they are able to consume A or B, they choose consumption B instead of consumption A for various reasons. It can be considered that the alternative theory has played an important role. The growth rate of luxury consumption in China from 2011 to 2019 is proposed and combined by the least square method to obtain the theoretical growth rate in 2020 and 2021. The comparison shows that the theoretical growth rates in 2020 and 2021 are significantly higher than the real growth rates. Finally, using the alternative theory, this paper expounds on the reasons for the increase of luxury consumption in the downward stage of the economic cycle from the aspects of psychological needs, consumption motivation, and the impact of COVID-19.

With the rapid development of computer technology and Internet technology, the financial management of modern enterprises and companies is gradually developing in the direction of intelligent management. As a result, the traditional financial management model can no longer adapt to the current financial information management. This paper takes the application of J2EE technology in finance as the research object, analyzes the technical knowledge (including J2EE technology, J2EE structure and database, and Java database connection) used in J2EE in financial application in detail, and analyzes the application of J2EE technology in finance. The system design in the application and the main advantages are analyzed in detail. This technology not only saves a lot of manpower, time, and cost, but also plays an extremely important role in enhancing the competitiveness of enterprises, improving the level of enterprise management, and promoting financial management techniques and methods, theoretical innovation, and concept renewal.

This paper analyzes the sentiment of texts in the Guba forum by using natural language processing, constructs the individual investor sentiment index, which combines post text, reads, and comments, and categorizes individual investor sentiment. Next, we introduce the stock quote distribution as an intermediary variable and demonstrate individual investor sentiment impact on stock price volatility in Simulated Stock Trading System. The study found that positive individual investor sentiment causes stock prices to rise quickly to near the limit-up price; individual negative investor sentiment causes stock prices to fall; individual neutral investor sentiment causes less volatility in stock prices.

Implementing digital technologies has made organizations more collaborative. Knowledge-based collaborative activities management has become the main organizational model of work. Identifying the factors that influence organizational collaboration is crucial to organizational design and ensuring effective digital transformation. Currently, collaboration has not received enough attention. Enterprises lack an understanding of its importance. This chapter quantitatively studies collaborative activities and their influencing factors. The regression analysis results show that the invisible independent variable such as knowledge-based innovation has a greater effect on organizational collaboration. We further conduct predictive analysis for organizational collaboration, and the predictive analysis results help the two case study companies to clearly understand their current status of organizational collaboration and the level of their respective influencing factors.

As a pillar industry of the country, the stable development of real estate is a matter of national security and livelihood. In recent years, real estate prices have soared to record highs. To effectively control the real estate price under this circumstance, it is of great significance to study the main factors affecting the real estate price. In this paper, we analyze the driving factors of China’s commercial housing prices from seven aspects: real estate development investment volume, commercial housing sales area, commercial housing completion area, per capita disposable income of all residents, total year-end population, consumer price index, and money supply, in response to the rapid growth of China’s housing prices. Based on the 20-year time series data of China from 2001 to 2020, the regression analysis of the influencing factors of house prices is conducted by using the econometric software EViews, and the influencing factors of commodity house prices are modeled by the stepwise regression method. The conclusion is that the sales price of commodity houses is mainly influenced by four factors: real estate sales area, real estate completion area, per capita disposable income, and consumer price index. Finally, the economic significance of the model is explained, and relevant policy suggestions are made.

In this paper, we empirically analyze the factors influencing real estate prices in eastern, central, and western regions of China using Stata Information Data Analysis software with fixed-effects panel regression models (FE) and mixed-effects panel regression models (POOL). We introduce lag variables, write program codes, and gradually analyze the main influencing factors that affect real estate prices in eastern China: The current and previous real estate planned investment, and the previous period’s housing prices. The main influencing factors of real estate prices in central China we analyze are disposable income in the current and previous periods, planned investment in real estate in the current period, and housing prices in the previous period. The factors that we obtained to influence real estate prices in Western China are the amount of planned real estate investment in the current and previous periods, the area of housing completed in the current period, and the house price in the previous period. In general, we find that current and previous real estate planned investments and the current and previous real estate planned investments are the main factors that affect real estate prices in China.

To explore the hot spots and trends of domestic and foreign research on the gig economy, this paper uses CiteSpace software to visually analyze the Chinese and English literature on this topic selected from CNKI and WOS databases. It is found that the gig economy generally refers to the digital gig economy based on the network platform in the Internet era. Research in this field officially started in 2016. A highly productive group of domestic and international authors has not yet been formed. The research institutions are mainly universities, and the cooperation between English research institutions is closer. Global-developed countries and domestically developed cities are the main force of research in this field. Compared with some countries, such as the US and the UK, China still has more room for development in terms of case studies and empirical research. The research on the gig economy focuses on the following points: first, the connotation of the gig economy; second, the relationship between the gig economy and the sharing economy, the digital economy, the platform economy, and their impact on the economy and society; third, the opportunities and challenges in the gig economy; fourth, the advantages and disadvantages of Internet technology in the gig economy network platform; finally, the new legislation needs under the gig economy, etc. Overwork, collective action of gig workers, and service performance of gig jobs are the latest research trends in this field.

China GEM has injected new vitality into the development of China’s capital market since its official establishment in Shenzhen Stock Exchange in 2009. However, due to the short development time of the GEM market and the fact that most of the listed companies in the GEM are high-tech enterprises and high-growth companies, their share profit distribution shows certain particularity. Therefore, this paper aims to study the dividend distribution policies that affect GEM-listed companies to further enrich the theoretical results of dividend policy and promote the development of GEM-listed companies. Based on reviewing the literature on the influencing factors of dividend policy at home and abroad, this paper selects 2,145 sample data from 2018 to 2021, uses SPSS 26.0 statistical software as a tool, adopts empirical research methods, and then studies the factors affecting the dividend policy of GEM listed companies in China, and puts forward relevant suggestions according to the analysis results, aiming to further improve the dividend distribution of GEM listed companies and optimize the allocation of resources.

In financial markets, all investors are concerned about the price and direction of the purchased asset. In recent years, in addition to investing in industrial assets, such as real estate and the stock market, some investors have put their spare money into virtual digital currencies such as the Bitcoin market in hopes of obtaining high returns as a form of online finance. The price of Bitcoin has changed dramatically over the past decade since the concept was first proposed in 2008. This paper discusses the advantages and risks of Bitcoin from the perspective of financial technology and argues that the skyrocketing prices of both are due to scarce supply, excess liquidity, and the formation of financial markets. This paper draws conclusions based on data analysis using the ARMA Models, as well as an understanding of Bitcoin. Although Bitcoin’s high price volatility may interfere with existing monetary policies and energy consumption confirmations, it also has advantages such as non-inflation, technical security, and facilitating blockchain development.

Usually, companies with poor financial conditions at the time of an epidemic issue bonds, and it is particularly important to reasonably measure the various risks associated with the issue of bonds. This paper uses the Conditional Cost at Risk (CCaR) risk measurement technology based on the Conditional Value at Risk (CVaR) idea to measure the cost risk of epidemic prevention and control bonds issued by enterprises in the western region during the epidemic. We design a Conditional Payment at Risk (CPaR) risk measurement model to measure the liquidity risk brought by the issuance of epidemic prevention and control bonds to enterprises. We also analyze the effectiveness of epidemic prevention and control bonds in the western region based on different bonds and come up with an accurate evaluation. By comparing the calculation results, it can be analyzed that the overall level of financing utility of anti-epidemic bonds issued by non-government financed enterprises in the western region is good, both in terms of the market risk of bonds and the cash liquidity risk faced by enterprises.

The paper utilizes a Python crawler to obtain and organize data from the CSMAR database. The PDF links of annual reports are extracted using Xpath, and the tabular data in the annual reports are extracted by embedding the “pdf plumber” programming in Python. Then, by building a regression model, we analyzed the issue of the influence of capital structure on the corporate growth of A-share GEM-listed companies. The result indicates that the capital structure negatively affects corporate growth in the context of Chinese GEM. However, company size, profitability, and return on investment are positively correlated to corporate growth. The result also shows that industry and ownership of property do not affect the relationship between capital structure and corporate growth. Finally, this paper suggests some policy implications and suggestions from the perspective of managers of GEM companies, government regulators, and investors.

In recent years, our country’s tourism industry has emerged, and it is leading becoming increasingly dominant in the modern economy, further promoting the development of related industries. With the financial support for the development of the tourism industry, we can learn that a basic test based on the panel data of the eastern, central, and western regions will show the efficiency of investment in tourism in the east and central regions of China has declined due to the rapid economic development. It is said that it has played a vital role; in the western region, it is not so rapidly developed and is supported by indirect financing by bank loans, but it is more or less important for the development of tourism. The tourism industry in the eastern and central regions of China has completely realized the transformation from external growth to internal development. The western region will tend to be more externally oriented. The regional differences are mainly analyzed from the selection of variables, using a general engineering approach to achieve the current status of financial integration. The data sources are mainly from cross-sectional unit models. The tourism industry needs panel data as support for the analysis using the variables of bank loans. As a tourism industry that is open to the outside world, more and more tourists will enjoy new tourism models, such as ecotourism, rural tourism, cultural tourism, etc., bringing financial and economic support to these new tourism models and making them further successful. In this paper, there is a certain gap in the development of China’s tourism industry in terms of financial support for the development of the tourism industry. Given the vigorous development of the tourism industry, this paper uses a modeling algorithm to analyze whether the results are consistent with regional differences to facilitate a better discussion. A brief overview of the current state of research and the theoretical model of financial support will be presented below.

Firstly, this paper summarizes the origin and significance of tourism economic big data, then proposes the application of tourism economic big data in the tourism industry to provide strong data support for tourism industry marketing, and elaborates on the existing problems of tourism marketing in the economic background, market environment, target consumer groups, the impact of digitalization on traditional marketing of the company, and puts forward a basic analytical framework for the construction and optimization of tourism industry digital marketing system. After that, specific marketing strategies are implemented based on tourism economic big data, oriented to the tourism economic market, and taking meeting customer needs as the starting point. B/S architecture is adopted to design and develop a modern marketing platform for the tourism industry. Based on the MVC design pattern, the Hadoop cluster architecture and Java language are applied to build the server of the platform, which is used to collect data and analyze the needs of tourism consumers. Through the analysis and prediction of the tourism economy big data, better tourism services are provided for customers. Based on this, a marketing system that meets the requirements of the current tourism economy market can be established, which can segment the tourism demand market, transform the tourism business economy, and promote the sustainable development of the tourism industry.

The establishment of pilot Free Trade Zones is one of the important measures for China to stimulate regional economic growth through deepening reform and opening up. In this paper, the panel data of 21 cities in Guangdong Province from January 2010 to December 2020 are taken as samples, and the fixed effect model is used to analyze the spillover effect of the Guangdong pilot Free Trade Zone on regional economic growth. The results show that the spillover effect of the Guangdong pilot Free Trade Zone on regional economic growth is significant, with the largest spillover effect on the home city, followed by the Pearl River Delta, which ranks third in Guangdong Province.

In this paper, a digital economy information platform is designed. First, the evaluation methods that can be applied in the digital economy platform are delineated through the analysis of the development prospect of the digital economy. At the same time, through the data accumulation of the digital economy, data mining and Internet of Things technologies are finally combined with evaluation methods to complete intelligent information analysis to improve the research on the information strategy of the digital economy.

With the intensification of market competition, enterprises must improve their competitiveness to survive in the fierce environment. Financial management is the most important and economical guarantee to ensure sustainable development and prevent risks. Traditional research methods for enterprise financial risk analysis and early warning mainly include statistical analysis and artificial intelligence models. A comprehensive, accurate and complete enterprise risk analysis model is constructed based on quantitative financial indicators. In addition, the research on enterprise financial risk analysis and early warning is affected by various factors inside and outside the enterprise, and the uncertainty is very high, and the excellent performance of data mining technology in the study of uncertainty theory makes the two closely connected. Therefore, this paper addresses the problems that traditional methods cannot solve, delves into association rule mining and time series analysis prediction models, and combines data mining techniques to analyze enterprise financial risks and help decision-makers develop reasonable investment strategies. At the same time, these algorithms can be applied to the study of enterprise financial risk analysis and crisis early warning. This paper proposes a conceptual hierarchical tree model of corporate financial risk and a financial crisis early warning model for dynamic maintenance of time series.

As live streaming becomes prevalent, the Internet celebrity economy developed quickly, and Internet celebrities have realized traffic monetization through e-commerce live streaming, but some negative impacts keep appearing. For example, the homogenization of live streaming content and the gradual vulgarization of the image of female anchors make people easily deceived by live performances, which in turn results in a large number of rejected products, suppresses people’s purchasing desire, brings more adverse effects on e-commerce live streaming, and impedes economic development. In order to drive the rapid growth of the live-streaming industrial economy, this research, based on the integration of the Internet and the economy, starts from the development status of live streaming by women aiming at selling goods and looks for the new development strategy of live streaming with goods marketing promotion with the combination of Internet technology and economic development to enable female network anchors to boost rapid economic development in the live streaming mode. As regulators, multi-channel network (MCN) institutions, and users are used to evolve game models, MATLAB is used to simulate stakeholder behavior.

In this paper, we propose a method to determine the portfolio risk of digital currencies on blockchain based on Gaussian mixture clustering and Barra multi-factor model. 36 digital currencies are randomly selected from 8 aspects, and the prediction data of currency prices are obtained by long and short-term memory recurrent neural network for a uniform time period of 84 days, and the goodness of fit is above 99%, which not only preserves the nature of each currency itself but also facilitates data analysis, The Garch model is used to calculate the volatility of currency prices and volume for the same time period, and the systematic risk factor is calculated by combining with the capital asset pricing model, and the Barra multi-factor model is used to construct the influencing factors on the basis of which the validity and linear correlation tests are performed to ensure that the most influential factors are selected. Gaussian mixture clustering is used to assemble the portfolio of currencies with the closest price and volume volatility and expected return. A multi-factor model was used to predict the expected return of each currency in a single portfolio over the same time period, and the portfolio performance analysis and value-at-risk theory were combined to further analyze the strengths and weaknesses of the portfolio. The results show that the model prediction accuracy is 90.0% under the influence of the assumptions of style factors (profitability R_it, price daily volatility $\frac{\sqrt{V_L+{\sigma }_n}}{2}$, systematic risk factor β and statistical factors such as size a_it, momentum RSTR_it, liquidity L_it).

The Belt and Road Initiative (B&R) aims to unite countries and regions along the route to form a community with shared destiny and to achieve mutual benefits. As a macroeconomic barometer, the stock market can reflect the fluctuations of the real economy and predict the macroeconomic development trend. To timely identify the opportunities and challenges brought by the B&R to the stock markets of the countries along the route, this paper utilizes a Python crawler to obtain stock price indexes of eight countries and analyzes the fluctuations of the indexes. This paper divides the data into two parts, namely before and after the countries joined the B&R initiative, and uses GARCH-VaR models to calculate the stock price index risk of each country using WinRATS. The empirical results show that the impact of the B&R on stock price index risk varies across the eight countries. In addition, the price volatility of CSI 300 after the countries participated in the B&R initiative is much lower than that before the participation, and the stock price index risk of some sample countries is also reduced. Finally, the paper makes some suggestions from the perspective of the government and foreign investors.

This is a project on the use of the Monte Carlo scheme to price exotic options to be completed using Python. Both values of the Asian option and the Lookback option are calculated by using the Euler– Maruyama scheme for initially simulating the underlying stock price. In both cases, the following set of data is used to simulate underlying stock prices. Today’s stock price S0 = 100, strike price E = 100, Time to expiry (T − t) = 1 year, volatility sigma = 0.2, and constant risk-free interest rate r = 0.05.

Artificial neural network (ANN) is a prevalent tool because of its extensive adaptivity and outstanding performance. According to previous studies, Long Short-Term Memory (LSTM) neural networks generally perform well in forecasting financial time series than other models. However, few studies apply LSTM to CPI and price level forecasting. This paper separately constructs the LSTM and the Vector Autoregression (VAR) model, a classic econometric approach for time series forecasting, based on 23 factors that affect CPI directly or indirectly. The results show that the error of the LSTM is significantly lower than that of the VAR in forecasting China’s CPI, while the VAR model provides an explicit explanation of the factors of CPI forecasting through the Granger causality test. Additionally, a synthetic model combining the advantages of both generates a more satisfying outcome. This paper forecasts the CPI by combining the LSTM and VAR models for the first time and provides a new reference to the inflation forecasting area.

With the rapid development of global digital transformation, digital transformation is a system engineering with many collaborative elements, high degree of complexity and great difficulty in overall planning. As a common language for collaborative dialogue between different systems and different elements, standardization is an indispensable soft foundation in digital transformation, and also an important starting point to realize its efficient, intensive and collaborative development. Digital government is the key basis and an important part of the digital transformation, which has received widespread attention from all countries in the world. Based on the analysis of relevant policies and measures in United Kingdom, Denmark, Australia, Singapore and other countries with good performance in digital government construction, combined with the situation of digital technology standardization, this paper puts forward suggestions on the construction of digital government standardization, so as to provide reference for shaping fair digital transformation and building government digital capability. This paper proposes a digital government construction standard system, the evaluation index system of standard application, and the BP neural network model of standard application evaluation is constructed, and the simulation test is carried out.

China has entered the era of network information, and search engines have become an important way for the vast number of network users to obtain information, while search keywords reflect the main concerns of users and the tendency of their consumption behavior. The analysis of keywords in online search engines can also be used as one of the bases for the identification of macroeconomic problems. The development of real estate is related to the national macroeconomic trend and the vital interests of residents. Therefore, we should actively apply advanced information technologies such as big data and BP neural network to scientifically extract and standardize the network keywords for a more accurate prediction of the commodity housing price index and promote the sound development of China’s real estate industry.

In this paper, the panel data of 21 cities in Guangdong Province from the first quarter of 2010 to the fourth quarter of 2020 are taken as samples, and the system GMM of the dynamic panel data model is used to make an empirical study on the effect of Guangdong Pilot Free Trade Zone on local economic growth, and the conditions of the effect. The results show that the effect of the Guangdong Pilot Free Trade Zone on local economic growth is significant. The effect on the mother cities is the most obvious, followed by the Pearl River Delta, and Guangdong Province ranks third. At the same time, the Guangdong Pilot Free Trade Zone and the level of trade openness have a complementary effect on pulling the growth of the local economy, and the pilot Free Trade Zone and the size of the government have a substitution effect on economic growth.

In order to improve the influence of economic green transformation from the perspective of coordinated development of the industrial economy, an evaluation model of the influence of economic green transformation from the perspective of coordinated development of the industrial economy is constructed. An impact evaluation model of economic green transformation from the perspective of industrial economy coordinated development based on a clustering algorithm is proposed. A big data parameter collection model for the impact evaluation of economic green transformation in the perspective of coordinated development of the industrial economy is constructed, a structural block feature reorganization evaluation model, and adopt fuzzy C-means clustering method to realize the cluster analysis of the big data for the impact evaluation of economic green transformation in the perspective of coordinated development of the industrial economy is established. The cluster center optimization of the big data for the impact evaluation of economic green transformation in the perspective of coordinated development of the industrial economy is realized, and the candidate target set of the big data cluster area for the impact evaluation of economic green transformation in the perspective of coordinated development of the industrial economy is traversed, and the impact on economic green transformation in the perspective of coordinated development of the industrial economy is realized according to the different levels of clustering characteristics.

The application of information technology can effectively alleviate “information asymmetry” and reduce the uncertainty of technology, customers, competition, and resources faced by enterprises in the process of innovation. Based on the analysis of market orientation, strategic flexibility, and technological innovation, this paper constructs a conceptual model and presents its relevant hypotheses. We have conducted a large-scale questionnaire on various enterprises across many provinces and cities in China and got 405 valid questionnaires. This paper firstly uses SPSS22.0 to conduct a preliminary analysis of the questionnaires and evaluates the reliability and validity of the model. Then this paper performs descriptive statistical analysis on the data of each variable and obtains the mean, standard deviation, and correlation coefficient of each variable; as shown in Table 2, the correlation analysis results are consistent with the hypothesis in this paper. Then this paper conducts hierarchical regression analysis and hypothesis testing. As shown in Table 3, all regression models have passed the F test; the overall significance of each model is good, and each hypothesis has been verified. Empirical testing results show that market orientation is conducive to cooperative innovation and not conducive to independent innovation; market orientation is conducive to improving the resource flexibility and competence flexibility of enterprises; resource flexibility is beneficial for enterprises to undertake independent innovation and not beneficial for enterprises to undertake cooperative innovation; competence flexibility is beneficial for enterprises to take independent innovation and cooperative innovation. Finally, this paper puts forward four implications for China’s enterprises to use information technology to obtain market information to meet customer demands and promote technological innovation.

Correctly clarifying the relationship between tourism science and technology innovation, industrial upgrading and economic growth is the core issue facing China’s tourism supply-side reform and high-quality development. On the basis of combing the existing literature, in order to further explore the interaction mechanism among the three, this paper puts forward the general idea of incorporating the three into the unified framework and uses the panel data of 31 provinces, municipalities, and autonomous regions in China from 2008 to 2017 to construct a panel simultaneous equation model to test the relationship between tourism science and technology innovation, industrial upgrading and economic growth.

Collaborative innovation can effectively gather the advantages of technological resources of technology-based enterprises. In exploring the collaborative innovation behavior of technology-based enterprises, this paper considers the technology-sharing behavior of both enterprises. On this basis, a dynamic evolutionary game model of collaborative innovation in technology-based enterprises is constructed to explore the influence of changes in factors such as technology sharing degree and technology absorption degree on enterprises’ innovation decisions. The results show that the higher the degree of technology sharing and absorption, the more likely firms are to engage in collaborative innovation. Meanwhile, the amount of technology reserve and the degree of technology absorption can reflect the technological level of enterprises to a certain extent. The smaller the technological gap between enterprises, the more likely they are to achieve cooperative innovation. In addition, the cost of inter-enterprise cooperation will have a negative impact on the cooperative innovation of enterprises. Thus, in order to better promote collaborative innovation, an open digital collaborative innovation platform can be established to facilitate communication between enterprises.

The development of the rural cultural industry has a vital role in promoting the implementation of a rural revitalization strategy. Under the background of digitalization, using digital technology to empower the development of the rural cultural industry can promote the high-quality output of the rural cultural industry, optimize the transmission and dissemination of rural culture, improve the outcome of rustic cultural patterns and cultural sectors, and make the rural culture come to life. To break through the barriers of the rural cultural sector and realize the integrated development of the rural industry is to improve the infrastructure, use digitalization to transform rural culture into digital scenes, such as digital platforms and digital memory museums, and move rural culture to the network cloud. The second is to strengthen digital literacy and skills for farmers, enhance digital countryside publicity, and attract and create cultural industry-related talents. The last is to integrate the development of rural industries, while the rural cultural sector under digital empowerment will usher in more significant economic and social benefits.

This study selects Guangzhou as the research object based on the carbon emission accounting method in accordance with Guangzhou, using the LMDI model and the Tapio decoupling model to investigate the driving factors of the indirect carbon dioxide emissions of its residential living energy consumption. Research shows that: Guangzhou residential indirect energy consumption accounts for a high proportion of carbon emissions (60%–73%), which is more worthy of attention; its consumption structure needs to be optimized and upgraded to achieve energy conservation and emission reduction; other driving factors other than energy consumption intensity. There is more room for emission reduction; the decoupling relationship between indirect energy consumption carbon emissions and consumption expenditures of residential lives is unstable, and the overall situation is positive. Further, we put forward countermeasures and suggestions to promote the reduction of carbon dioxide emissions from the indirect energy consumption of residents in Guangzhou, guide the optimization and upgrading of the consumption structure, advocate green consumption, promote the carbon inclusive system, control the intensity of energy consumption, promote energy conservation and emission reduction, control population scale and optimize population structure.

This study analyzes the coordination of the digital economy and green innovation efficiency using provincial data from 2011 to 2020 in China. The study chooses the entropy weight method to build an evaluation system for the digital economy. The SBM-DEA is used to calculate the green innovation performance of each province. Moreover, the spatial and temporal differences in the coupling level are analyzed. The results reflect the coordination of China’s digital economy and green innovation efficiency continuously growing during the study period. The regional characteristics show that the eastern region shows more coordination compared to other regions.

The factors affecting economic growth have been given attention and generated lively discussions by many economists, but the impact of technological progress and financial development on economic development in the context of the Chinese economy has been less discussed. In this paper, we use a fixed effect model to empirically test the impact of technological progress and financial development on economic growth using provincial panel data for 31 provinces in China from 2010 to 2019. The result shows that the traditional factors affecting economic growth, capital and labor, have a positive impact; scientific progress also contributes to economic growth; but financial development can inhibit economic growth.

Under the impact of technology and the pandemic COVID-19, the net economy has developed rapidly, especially in e-commerce. The development of e-commerce has greatly facilitated people’s lives, especially during the epidemic. But the growth rate of e-commerce sales during shopping festivals in the first half of this year dropped significantly. To analyze the reasons for this decline in the growth rate of the net economy and to predict whether there is a possibility of a decline in the sales of the e-commerce shopping festival in the second half of the year, we selected the household storage data of Guangdong Province in the last four months to construct a grey prediction model. The data analysis results show that residents’ willingness to store keeps rising every month and will continue to increase in the next two months. And the sales of the e-commerce shopping festival in the second half of the year will decrease yearly. Aiming at this problem, we suggest that the promotions of merchants should be simpler to stimulate consumers’ purchase desire.

Under the background of economic globalization and rapid industrial transformation, the cultural industry has gradually become a new pillar industry in various countries. However, how to realize the rapid development of the cultural industry needs to be further investigated. This study uses the grey correlation analysis model to study the related development of scientific and technological innovation, the total demand of the cultural market, the existing cultural resources, the policy environment of the cultural industry, and the supply of special high-end talents. We find that these five indicators are highly correlated with the development of cultural industries. And then five suggestions are put forward, namely strengthening the national policy support and consolidating the cultural industry system foundation; excavating the existing cultural resources and laying a solid foundation for the cultural industry; enhancing the integration of production, education, and research, and cultivating high-end talents in the cultural industry; expanding the total market demand and opening up the development space of cultural industry. We will vigorously promote scientific and technological innovation and boost the development of cultural industries.

With the advent of the era of big data, the influence of the Internet finance and the digital economy is becoming stronger and stronger. At the same time, data analysis and cloud computing provide a powerful digital and scientific basis for the formulation and improvement of current social policies. The proportion of pension fund expenditures has always been in the first place in the social insurance fund expenditure. It is of great practical significance to statistically analyze the impact of pension fund expenditures on residents’ consumption in Guangdong Province. This paper selects six variables, such as urban and rural pension fund expenditures and residents’ consumption level, in Guangdong Province from 2010 to 2019, uses the statistical software Stata for data analysis, and establishes the OLS multiple regression model. At the same time, the empirical analysis shows the relationship between urban and rural pension fund expenditures and residents’ consumption levels. The data shows that the OLS model has passed the 1% significance level and has a good fitting effect. The modeling result with a high fitting degree further confirms the conclusion that increasing the expenditures of urban and rural pension funds can improve residents’ consumption level, which provides scientific and solid digital support for the development of the “Internet plus pension industry” in Guangdong Province.

Based on the unbalanced panel data of 35 medium and large-sized cities from 2015 to 2020, this paper empirically examines the relationship among the financial ecology environment, core competence construction, and the growth of enterprises, focusing on the impact of the improvement of the financial ecology environment on the resource allocation for enterprise development. It is found that a favorable financial ecology environment can induce enterprises to invest more resources in the construction of core competencies. And the construction of core competencies is a continuous process, which shows a lagging effect on the growth of enterprises. Therefore, formal institutional reforms in the political, economic, and social fields lead to the coordinated development and improvement of various aspects, such as the quality of government and social development environments, the active promotion of which can reduce the uncertainty of development and reduce transaction costs. So companies can focus on building core competencies to a larger extent.

The sustained volatility of maize prices in recent years has had a negative impact on China’s agricultural economy and livelihoods. To deeply investigate the risks in the maize market, the article uses the X-12-ARIMA seasonal adjustment method, the HP filter method, and the ARCH class model to analyze the characteristics of maize price volatility. The study found that: Since 2006, maize prices have generally been fluctuating and rising. There are obvious seasonal effects, indicating that domestic maize supply and demand have changed from oversupply to tight supply-demand balance, and supply and demand have a greater impact on maize prices. There is a significant agglomeration effect and negative asymmetric characteristics of maize price fluctuations, implying that negative news will cause maize prices to continue to fluctuate significantly. To this end, the article makes three suggestions: first, accelerate the scale and modernization of maize production and improve the risk resistance of the maize market, secondly, further play the decisive role of the market in resource allocation and reduce policy intervention, and third, build an authoritative open platform for maize market information and establish a market risk monitoring and early warning mechanism.

Singapore has developed a well-established public housing system built and managed by the government. In Singapore’s unique public housing system, a welfare loss has been observed in its current lottery mechanism used for public housing allocation, based on studies by other scholars. The house allocation is considered a two-sided matching. However, as the Singapore government set the ethnicity quota for public housing blocks based on its purpose of ethnic integration, the matching becomes a type of block-bound two-sided matching. Inspired by Singapore’s public housing allocation problem, this paper tends to establish a new model using the Deferred Acceptance mechanism to improve the social welfare obtained from the public housing allocation. And due to the existence of speculation in the public housing market, this study also tests whether our new model helps to reduce speculative house purchases.

To find out the distribution characteristics of FRESHIPPO, this paper studies the distribution of FRESHIPPO from the perspective of the digital economy. The paper uses big data methods to collect FRESHIPPO’s information and urban POI, uses factor analysis to calculate the comprehensive score of the main factor, and uses social network analysis to measure indexes of the network structure of FRESHIPPO (Degree Centrality, Betweenness Centrality, and Closeness Centrality). Based on the above score and indexes, this paper establishes coupling coordination models respectively to analyze the degree of matching between the service area of FRESHIPPO and the built environment.

Based on the value cognition and prospect theory as well as the panel data of 84 listed AI concept stock companies from 2011 to 2019, this paper analyzes and tests the impact of R&D intensity of AI enterprises on the innovation performance, and the moderating effect of value cognition and bankruptcy threat in this process. Studies have shown that the R&D intensity of AI enterprises is positively correlated with innovation performance, whereas the complexity of value cognition and the joint interaction between value cognition complexity and bankruptcy negatively regulate the relationship between R&D intensity and innovation performance in AI enterprises. The results of the research are of great theoretical and practical significance for enterprises to implement the strategy of enterprise R&D investment under the orientation of innovation performance.

As the largest egg producer and consumer, China has been playing an increasingly important role in the world egg industry. Due to rapid economic growth and consumer’s increasing demand, China’s laying hen industry is undergoing structural structure changes and many challenges. The stochastic meta-frontier (SMF) approach is used to examine the technical efficiency (TE), technology gap ratio (TCR), and meta-technical efficiency (MTE) of laying hen production in China from 2011 to 2020. The results show that although TE fluctuated irregularly during that period, TGR and MTE of large and middle-sized laying hen farms all showed an increasing trend. The regional differences in TE, TGR, and MTE are significant: the Northeast region is relatively higher than the other regions, and the Western is always the lowest; Heilongjiang takes the lead in the Northeast, while some provinces such as Guangdong and Hainan in the Eastern region are always at the bottom. Accordingly, some policy recommendations are put forward for achieving a competitive egg industry with high efficiency.

Disaster prevention and mitigation in rural areas plays an important role in agricultural development and rural revitalization. Most of the existing studies have studied the effect of disaster prevention and mitigation in rural areas, but there is a lack of comprehensive analysis of the benefits it can produce. This paper adopts the empirical analysis method to analyze the impact of rural disaster prevention and mitigation on the comprehensive income of villagers and studies the mediating role of villagers’ awareness of disaster prevention in this mechanism. Through the survey data of a village in Hubei Province, China, it is concluded that the rural disaster prevention and mitigation work has a significant positive correlation with the comprehensive income of the villagers, and the villagers’ awareness of disaster prevention has a partial mediating effect.

Supply chain finance business as a comprehensive financial solution for small and medium-sized enterprises or small and micro enterprises has been for many years, but in the new era of the information wave, the traditional model of supply chain finance has revealed its irreconcilable drawbacks, and the arrival of block chain technology seems to bring new solutions. Block chain technology has the advantages of immutable information, decentralization, strong traceability and so on. It can break the single vertical transaction upstream and downstream of enterprises, promote the upgrade of network structure, and create a financial industry chain that efficiently delivers value, so as to achieve a win-win situation among all participants based on credible and verifiable trade relations. Based on this, this paper gives an overview of the supply chain finance model and block chain technology, and analyzes the application of block chain technology in supply chain finance.

TargetTourism can drive the development of a region, and it also plays a significant role in promoting regional economic development. For the integrated development of the financial industry and the tourism industry, there are obvious data to prove that the technologies, applications, and markets based on the coupling coordination degree and gray correlation degree models are different, but the attributes of the two have common attributes and Features. From the point of view of common attributes, tourism and finance have both cultural and economic attributes, which is a comprehensive industrial technology. From the point of view of common characteristics, tourism and financial industries have regional, economic and creative characteristics. Specifically, it is necessary to carry out research from the perspective of engineering. From the perspective of a digital economy model, this paper conducts an empirical study on the relationship between financial development and regional tourism development. From the findings of this study, it can be seen that the development of regional tourism is closely related to the development of the financial industry.

This paper focuses on the most important three-party participants in supply chain finance, builds a dynamic evolutionary game model of financial institutions, core enterprises and small and mediumsized enterprises, compares the strategic changes of the three parties before and after the introduction of blockchain technology, and discusses how blockchain technology can empower supply chain finance. The mechanism of action and numerical simulation analysis were carried out with the help of Matlab. The study found that the application of blockchain technology can effectively break “information barriers” and help financial institutions build a credit system; strengthen the cross-level transfer of core corporate credit to solve credit problems; reduce the financing cost of small and medium-sized enterprises to improve financing efficiency, thus achieving a stable state.

American options are important financial products traded in enormous volumes across the world. Therefore, accurate and efficient valuation is of paramount importance for global financial markets. Due to the early exercise feature, the pricing of American options is significantly more complicated than European options, and an analytical closed-form solution is unavailable even for simple dynamic models. Practitioners employ various valuation methods to strike the balance: accurate valuation usually suffers inefficiency, while fast valuation likely leads to inaccuracy. In this paper, we provide an innovative solution to address both the accuracy and efficiency issues of pricing American options by applying quantum reinforcement learning. Meanwhile, the quantum part of the new approach would potentially speed up the calculation dramatically.

Business model innovation is crucial for transforming Chinese smart manufacturing listed businesses in light of the local and global double-cycle economic model background. Numerous studies have shown that various factors affecting business model innovation are driven by each other to form multiple complex systems. In this paper, we analyze the driving mechanisms of business model innovation grouping in innovative manufacturing listed companies in a global context by exploring the internal and external factors from the perspective of internal and external drivers. At the same time, the fuzzy set qualitative comparative analysis (fsQCA) method is combined to find and found different abilities to the business model innovation mechanism of innovative manufacturing listed companies.

Plants absorb carbon dioxide through photosynthesis and convert it into organic matter, which is stored in the plant body as well as in the soil and water environment. This process is called carbon sequestration. Proper tree cutting is beneficial to achieve more carbon sequestration in a certain period of time. The purpose of this paper is to use modern computer algorithms to find the balance between the amount of deforestation and the maximum carbon sequestration and maximum benefit through mathematical modeling, and to give a more reasonable and universal forest management plan to balance and maximize the carbon sequestration benefits, economic benefits, and ecological benefits of forests.

Internet fund sale agencies have developed rapidly in recent years. But what factors drive investors to invest in funds through Internet fund sale agencies, and how do these factors shape the intention of the investor to action? Considering TPB and TAM models as basic factors, this paper combines trust theory and potency theory to explore how basic perception characteristics shape consumer trust and consumer behavioral intention through perceived risks and perceived benefits and discusses the logical relationship among them. The results show that investor trust plays a key role in consumer behavioral intention, while investor trust is shaped by the perceived risks and perceived benefits of investors, and different perception characteristics will have different impacts on perceived risks and perceived benefits, respectively.

As a pillar industry of the service industry, the tourism industry occupies an extremely important position in my country’s economic market. Nowadays, more and more enterprises are dominated by tourism services, and there is fierce competition among enterprises in the tourism industry. To occupy a place in the market, companies have established their marketing models to promote their brands, attract consumers and increase their popularity. However, the current marketing model in the tourism industry generally suffers from the lack of specific and phased planning, too many limitations, single means and channels, and a poor sense of proportion, which need to be solved. In this paper, by developing a big data marketing system for tourism enterprises based on Hadoop, we use big data and data mining tools to collect, analyze and extract effective tourism-related data and predict the future trend of tourism industry changes, help corporate marketing departments establish accurate tourism marketing Model, change the current marketing situation, improve the visibility of enterprises in the tourism industry, and make enterprises in the fierce tourism industry competition.

It is difficult to find rules in the huge stock system, but we can enter from a small entrance, explore the inextricable links between the market return and some time series variables, and predict the overall return based on these variables. This paper first explains the relationship between the following economic and financial time series variables and market return, then explain the reasons for choosing them: CPI, momentum, dividend rate, market book ratio, P/E ratio, a turnover rate of trading volume calculation, market-capitalization-weighted idiosyncratic volatility, logarithm of stock market liquidity index, Amihud illiquidity index, skew; secondly, this paper uses Python to make descriptive statistical analysis of these variable data; Thirdly, it plots variables and yields to compare trends; Then, it constructs a neural network model by multiple regression to select significantly correlated variables; Finally, the BP neural network is used to predict the return of China’s stock market.

Investment is a necessary way to promote the transformation of the machinery manufacturing industry to be intelligent, green, and low-carbon. This paper creates an investment capability information index based on big data to measure the quantity and quality of investment, then combines financial indexes to construct an investment capability evaluation system. On this basis, SPSS software is used for factor analysis and comprehensive evaluation of the investment capability of the machinery manufacturing industry. The results show that investments in shares, restructuring, and M&A in the machinery manufacturing industry are more frequent than that asset replacement and among the sub-industries, there are great differences in investment capability.

With the development of big data technology, the number of Internet users in China has exceeded 1 billion. With the help of a mature Internet community, the commercial value of public opinion dissemination is gaining more and more attention. It has attracted many scholars in the fields of journalism, social psychology, law, computer, and programming. How to identify favorable or harmful information, how to guide the correct public opinion, and how to turn public opinion into commercial value have become research hot topics in recent years. This paper sets the research object as the hot topic data on the Internet community Zhihu and calculates the relative public opinion dissemination efficiency level in each DMU through the DEA model. Then, these topics would be grouped into eight categories so that we could analyze the relationship between the number of hot topics and the percentage of high-efficiency topics. Finally, based on the statistical results, we obtain three main modes of public opinion communication and we infer the principle of how the mode forms.

As a trading method, quantitative investment has been widely used for more than 30 years, and its investment performance is stable. As the scale of the international financial market continues to expand, more and more investors have been recognized. Therefore, using quantitative decisions to trade financial products is the mainstream direction in the future. Taking gold and bitcoin, for example, we first establish a prediction model. Since there are limited historical prices to refer to at the early stage of trading, we adopt a robust regression strategy of moving averages to buy stocks. When we have commodity prices for more than 200 trading days, we use BP neural network to predict the price of the next trading day. This prevents the data from being too far back in time and affecting our current price trend. We then use polynomials to fit our predicted product prices and compare them to the actual values to evaluate the prediction model. Finally, with our quantitative decision model, our assets increased from $1,000 in September 2016 to approximately $192,922 in September 2021, which can be proven to be an excellent strategy. For Question 2, we believe that investors have the highest probability of profiting from this investment if we accurately judge future commodity price movements. We use a polynomial to fit the scatter plot of the product price predicted by the BP neural network. We perform the KS test, reliability analysis, and correlation analysis on the fitted curves and the actual prices of the products. It is found that our prediction curve is well-fitted. For sensitivity analysis, we change the transaction cost, finding that the transaction cost is negatively correlated with our income using the previously constructed prediction and decision model. When transaction costs appear, we should adjust our investment strategy in time to avoid frequent trading. An increase in transaction costs results in a small decrease in revenue; therefore, commissions are not sensitive to the final revenue. In conclusion, we provide a memorandum to help traders better understand and apply our quantitative trading strategy.

Diversification strategy has always been the focus of enterprises. Many enterprises have changed their models and adopted diversified management methods to improve their market competitiveness. However, not all companies can bring profit growth. For companies, a diversification strategy is like a double-edged sword. If it is not used properly, it will also lead the company into a financial crisis. This paper takes LeTV Group as the research object to discuss the financial risks and consequences of corporate diversification strategies and uses LeTV’s financial data to analyze the economic consequences of LeTV’s diversification strategy financial risks. The study finds that due to the mismatch of capital allocation, limited profitability, and insufficient funds as support, the financial risks of LeTV have increased. In response to a series of problems, this paper proposes measures to solve the financial risk control of LeTV’s diversification strategy. It is realistic and necessary to study the diversification financial risks and consequences of LeTV Group, hoping to provide a reference for the diversification strategy layout of other companies.

To analyze the economic risks in daily life, this paper fully considers the retail economic problems, such as commodity browsing, commodity classification, commodity purchase, personal center, etc., establishes an economic risk analysis model based on a collaborative filtering algorithm, finds targeted related risks through the analysis of personalized risks, and timely solves the economic risks in daily life. The experiments show that the method proposed in this study can effectively solve the problem of economic risk, and has good practical applicability.

This paper investigates the relationship between management over-optimism, corporate risk-taking, and R&D investment by using natural language processing technology. This paper uses Python 3.9 to extract the frequency of optimistic words to measure the emotion of management in the text. The results of the Stata 16.0 analysis indicate a significant positive correlation between management’s overly optimistic expectations and R&D investment and how corporate risk-taking mediates this relationship.

New financial forms such as Internet finance, which is supported by advanced technologies such as Internet information technology, big data, information security, and artificial intelligence, impact the transformation of the financial field. By using various technological means to innovate traditional financial services and products, Internet finance can realize its value in the financial field and alleviate the problem of corporate financing constraints. Based on the analysis of the application mode and existing advantages of Internet financial information technology, this paper draws on the main application characteristics of Internet financial information technology in alleviating corporate financing constraints with a combination of the World Bank survey data on Chinese enterprises and the China Digital Finance Research Center compiled by Peking University Digital Finance Research Center. The indexes are used to analyze the feasibility of alleviating corporate financing constraints based on Internet financial information technology. The study found that the channel that the application of Internet financial information technology can help enterprises alleviate financing constraints, which are dependent on the coverage and depth of Internet financial information technology.

In order to realize the reconstruction and innovation of business and management in the auto industry driven by big data, this paper uses Internet thinking and user thinking to create the ultimate user experience for users. As the data on the digital transformation of the auto industry and the product concept of auto users are collected, the transformation status of the auto industry is analyzed with a combination of the big data platform. We use big data to set up a complete set of auto industry inner logical architecture digital transformation, the operation management digitalization, digital marketing, and product innovation digital three aspects to analyze the industry transition way based on the user perspective. The purposes are to help the auto industry a more agile adjustment, to achieve improve the enterprise benefit internally, to create new user value externally, to increase organizational resilience to adapt to external changes, and to provide recommendations for the digital transformation of the automotive industry.

Firstly, in the introduction part, this paper briefly describes the advantages of the development of micro-credit enterprises under the background of the Internet, and then puts forward the idea of designing an Internet financial information management service system for micro-credit enterprises. The whole system design and development process follow the principle of taking the Internet as the background and information security as the starting point. Based on the full consideration and analysis of the overall and detailed requirements of the information management of users’ microfinance and enterprise financing services, the core service content and business process of the financial information management service system are fully realized by using the B/S design ideas and J2EE technical specifications. The framework is supported by SSH, and Tomcat is selected as the web server and Mysql database to realize background storage. The system can effectively help enterprise managers to sort out customer information and internal financial information of enterprises, integrate information management before, during, and after lending, realize the real timeliness, information security, and office automation of information, improve the office efficiency of managers, provide a new idea and open up a new path for micro-credit enterprises to do Internet financial information management.

Big data mining and analytics can be used to uncover hidden patterns and correlations in business, servecing as the optimal tool to interpret the behavior of companies in specific environments. Based on the large amount of data obtained from various sources, this paper examines the relationship between corporate participation in targeted poverty alleviation (TPA) and the tone of CSR reports. Python software is used for data collection, text analysis, and word frequency statistics. Results show that the tone of CSR reports is negatively related to firms’ participation in targeted poverty alleviation. This relationship is weaker in companies where executives have early poverty experience. Our findings shed light on how firms react to the adverse effects of poor TPA performance by manipulating intonation.

Economic development aims to follow the development mechanism of the market economy, produce more social wealth with low consumption, and keep the economy growing steadily. In this paper, the particle swarm algorithm is used to improve the BCC algorithm so that the improved BCC algorithm has a stronger optimization ability. Therefore, the improved BCC algorithm is used to search for evaluation indicators (EI) suitable for evaluating the development level (DL) of the low-carbon economy (LCE), and the integrated index system builds an LCE. Based on the three subsystems in the system, the LCE development level of a province is studied. The result is that the province’s LCE development level has realized certain achievements. In general, to achieve an LCE, it is necessary to promote economic and social development to achieve sustainable development through energy structure transformation and economic transformation.

Data in the Internet era is expanding at an alarming rate, while showing a trend of diversification and fragmentation. How to rationally use this intricate information and effectively apply it to work is important. It is imperative to give full play to the value of big data. Against the backdrop of the above background, a regional industrial macroeconomic decision-making method based on big data is proposed. Using data mining and intelligent analysis methods, we can obtain the development trend of the regional industry, and then obtain the development trend of the industry. This paper believes that in the era of big data, combined with the basic knowledge of big data, systematic research on the analysis of industrial economic information will help the application and practice of industrial big data.

“The steady development of human capital and science and technology” has always been the two recognized driving forces of economic growth, and the corresponding endogenous growth theory is also the mainstream method to explain economic growth. However, with the development of society, the dynamic equation of technology and human capital in the traditional model of “learning by doing” is insufficient to explain. To study the dynamic evolution rules of technology and human capital, considering the physical and chemical technology, the scientific research resource, education investment, technology and talent selection factors, we summarize the function form, develop a new framework of endogenous growth model, and carry out the preliminary analysis of the formation of dynamic optimization problems as the general framework of multidimensional differential equations. Finally, the steps of computer analysis of multidimensional nonlinear differential equations are given to provide the basis of economic theory analysis.

The coordinated development of taxation and the digital economy is related to the new pattern of China’s economic development. With the deepening of the integration strategy, the informatization construction of the tax system has made great progress. At the same time, however, the environment of informatization construction is constantly changing, especially in the era of the “digital economy”. With the promotion of tax reform, the application demand of tax informatization is increasing, and problems such as information islands and information asymmetry are gradually emerging. The system designed in this paper uses Hadoop data processing cluster and JavaWeb to develop a comprehensive tax data analysis system based on big data. Aiming at the problem of tax data informatization, three main functional modules, data integration, data inspection, and data query, are developed to centrally manage the data and provide inspection functions, so as to improve the quality of tax data and effectively help local tax authorities to comprehensively analyze the data of all parties. The platform has the advantage of highly centralized data, realizes data query function, improves the quality of tax-related data queries in the tax system, and further improves the quality and efficiency of tax supervision of regional tax authorities, thus promoting the development of the regional economy in China.

At present, the supply chain financing model is still in the initial stage of development in China. To effectively improve the operation level of the supply chain financing model and make it paramount in the financing of small and medium-sized enterprises, blockchain technology is introduced, and the overall structure of the system is firstly designed comprehensively, and then combined with the supply chain. According to the actual needs of financing, how to realize the main function modules of the supply chain financing system is discussed, and finally the system is tested in practice.

Buyers of global drugs must pay big licensing fees. As the race is different, at least before the drug launch in China buyers should ensure the effectiveness and safety of drugs, and what needs to be done is at least four phases of the clinical trial. If there is insufficient overseas clinical trial data, NMPA in China also needs RCT results or validation of drug safety issues of clinical studies. If a large amount of authorization fees is spent in the early stage and the sales volume is not good after it is listed in China, it may be a failed license. Therefore, it is necessary to consult before buying and help buyers make decisions based on data, policies, and expert opinions. We analyze the domain of the disease, learn from epidemic diagnosis to treatment and prognosis of process and data, production for the domestic top clinical experts, statistics, registration experts, policy adviser to the questionnaire, interview, and then summarize the research results. According to the expert opinion, we can give advice on whether it can be licensed in China successfully. It is recommended that the original factory prepare the materials needed for registration in China as soon as possible, and explain the controversial aspects of the original factory’s registration trial design. The overall recommendation is: if the original manufacturer can provide all the overseas materials required for the drug to be registered in China, the explanation of the controversial aspects of the registration trial can be accepted by Chinese experts, and the domestic manufacturer has the money to do both the RWS and the small RCT, the drug is still worth licensing in. We use NLP and knowledge graphs to calculate the commercial decision. Before developing a program we use data and knowledge to evaluate every KOL to select suitable ones. Data was collected from Pubmed and the introduction from portals of hospitals.

With the continuous development of the national economy, the social demand for electricity continues to increase, and the scale of investment in the power grid is also increasing. However, in recent years, the growth rate of electricity sales has slowed down due to the influence of national macro-control and industrial structure adjustment. In order to solve the problems faced by power companies in power investment management, this paper uses artificial intelligence technology to build a power company’s investment capability model. After summarizing the factors that affect the company’s investment, the calculation method of the relevant investment capacity of the power company is put forward. Through the application of the investment capacity model of the power company, it is estimated that the fixed assets will increase by 31.5 billion yuan, 31.1 billion yuan, and 32.4 billion yuan, respectively, in the next three years., totaling 95 billion yuan. After deducting inventory depreciation and new assets, the investment in the next three years will affect the company’s net increase in fixed assets of 11.7 billion yuan, 9.2 billion yuan, and 8.2 billion yuan, totaling 29.1 billion yuan, the audited investment scale in the next three years will be 41.7 billion yuan, 41.3 billion yuan, and 43 billion yuan respectively, totaling 1,260 yuan. The construction and application of this model are beneficial for power enterprises to optimize the allocation of limited resources and achieve precise and lean investment management.

The explosion of NFT artwork has enabled it to start a high-speed development model. As an emerging thing, NFT artwork has attracted widespread attention at home and abroad. This article sorts out the development of NFT artworks, analyzes the technical advantages of blockchain, and also reveals that there are still existing problems such as hype and institutional irregularity in the NFT artwork market. And based on the development status of NFT artworks, it will think and look forward to its future development trend towards the Metaverse.

The existing wireless temperature detection system of coal mines based on a Mesh network does not have the ability of host computer monitoring, in order to give full play to the function of the real-time collection of temperature data by hardware facilities, a supporting software system is designed and developed using Java programming language. Using swing-based GUI development, TCP/IP Socket communication, MySQL database, and other key technologies, the software can edit the industrialized graphical interface, display the working status of the temperature probe and the collected temperature data in real-time to the corresponding position, and has the function of regular storage and report statistics. Through the analysis of historical data in the monitoring area, this research grasps the spontaneous combustion law of coal mines and predicts future temperature trends, providing an important means to avoid fires.

Sentiment analysis refers to the information processing of people’s views, evaluations, or attitudes towards certain substances or their properties to extract sentiments and opinions. With the development of deep learning technology and the innovation of neural network structure, the focus of sentiment analysis methods has gradually shifted from traditional machine learning models to deep learning models and has gradually become popular, making sentiment analysis gradually applied to the market economy, politics and different fields, such as science, health or history. It can promote the development of society and generate new research directions. This review is divided into three parts: (1) Introduce the definition of sentiment analysis and the pre-processing of sentiment analysis. (2) Explain and discuss the models and methods of sentiment analysis and evaluate the ability of the model. (3) Summarize the advantages and disadvantages of deep learning models and propose sentiment a new direction for analysis.