Narrow Results

With the advent of the big data era, data-driven decision-making and analysis are increasingly valued in various fields. Especially in the field of education, how to use big data technology to better understand student needs, optimize the education process, and improve education quality has become an important research topic. This paper will explore the application of decision trees and related analysis algorithms in the analysis of college students’ physical fitness, in order to provide scientific basis for improving the physical health level of college students. This paper studies the application of DT (decision tree) and correlation analysis algorithm in the analysis of college students’ physical fitness. In this paper, the method of big data and DM (data mining) is proposed to extract the rules contained in the data information, so as to directly provide auxiliary decision-making for physical fitness test and analysis. The research results show that through training the training set, a good classification accuracy rate is achieved, and through optimizing the depth, the accuracy rate can reach more than 85.033%. Using DM technology as a carrier, this paper digs into the rules behind the new knowledge of college students’ physical fitness, and digs out the previously unknown, implied and potentially useful information and knowledge.

With the rapid development of society and cultural diversity, local opera, as a representative of traditional art, faces dual challenges of inheritance and innovation. The inheritance of traditional Chinese opera often relies on oral instruction, but in modern society, this method is no longer suitable for the rapidly changing cultural environment. The purpose of this study is to explore in depth the innovative cultural dissemination path of local opera through data mining techniques. This paper uses data mining techniques to explore and analyze the inheritance of innovative culture in local opera, and to study the inheritance paths. This paper proposes a data mining-based method for analyzing and identifying the paths of inheritance of innovative culture in local opera. This paper focuses on two aspects of technology clustering and technology theme association, and the research paths are divided into flexible clustering based on data enhancement and technology evolution path identification based on theme extraction. Firstly, the heritage changes of dating tunes are discussed. Secondly, the unique artistic esthetics of the dating tune is compared with the artistic characteristics of Henan opera and the three-stringed book. Through the research and learning experience of the Dai Yongqu, it is easier to thoroughly understand the artistic essence and cultural connotation of the Dai Yongqu, and that exposure to the Dai Yongqu is a process of touching traditional culture. In the text clustering section, the data co-occurrence frequency matrix is constructed based on the results of multiple clustering, transforming the problem of determining the number of clusters into a co-occurrence frequency problem with semantic information of the text, thus achieving the purpose of flexible clustering on demand. In a comparison with existing methods, the text representation method using data-enhanced coding achieves better semantic coding results. The flexible clustering method can differentiate the data better than the clustering method with a defined number of clusters and obtains clustering results with high intra-class similarity and high inter-class differentiation.

In this age of big data, education researchers are reconceptualizing and re-evaluating the value of education data. Therefore, we need to use educational data mining methods for data analysis to better guide teaching. The informatization level of colleges and universities is improving year by year, and the entire training data of students from enrollment to graduation is stored. These datasets are collected, stored, and kept by different departments, contain a large amount of regular and relevant information, and truly record the growth footprint of students. Traditional educational decision-making has not yet fully explored and used the valuable information hidden in data resources. Although some scholars have carried out research related to campus data mining at this stage, there are still many problems that have not yet been solved in the application of decision-making in colleges and universities. This paper is based on the idea of data-driven decision-making, combined with the data characteristics of campus big data, and establishes a model solution for student behavior analysis and behavior prediction by applying multiple machine learning algorithms. On the basis of the analysis of students’ academic behavior performance in the context of multi-category educational data, we proposed a cluster analysis framework for processing multi-type campus big data, and described the group characteristics of the clustering results. By introducing the K-prototype algorithm, we effectively solved the multi-category problem where traditional clustering algorithms (such as K-Means, etc.) cannot adapt to the attributes of educational data. The research results show that innovative educational decision-making models and methods are based on the idea of “data-prediction-decision”, which promotes the application research of big data science in the area of education.

Track and field teaching has always been an important part in school physical education (PE). With the deepening of curriculum reform and the continuous growth of IT, universities have gradually broken the old instructional mode, and have set up online teaching platforms and developed new instructional modes. How to integrate modern teaching and learning theory into the new teaching technology platform is the requirement of the times and the inevitable theme of the current PE reform. In this article, the track and field instructional resources under the platform of instructional resources management are studied, and the classification mining algorithm is used to mine and analyze the students’ interest data, so as to find out the rules and patterns of users’ access to instructional resources, thus further optimizing the allocation of users’ access to instructional resources, improving the efficiency of users’ access to instructional resources and the utilization rate of instructional resources. Experiments show that the improved collaborative filtering (CF) algorithm based on deep learning is superior to the other two algorithms in recommendation error, and the error is reduced by 10.69% compared with the traditional CF algorithm.

The application of network data mining technology further enhances the functionality and intelligence level of microsystems software. This paper first introduces the basic principles and key technologies of network data mining, including steps such as data collection, preprocessing, pattern recognition and result interpretation. Subsequently, a detailed analysis was conducted on the application scenarios of network data mining in microarchitecture software design, such as user behavior analysis, personalized recommendations, and anomaly detection. By mining user behavior data in the network, microsystems software can more accurately understand user needs and provide higher quality services. In addition, this paper also explores the optimization strategies of network data mining in the design of microarchitecture software. For example, by improving data mining algorithms, the accuracy and efficiency of data processing can be improved; utilizing cloud computing and other technologies achieves rapid processing and analysis of large-scale data; and combining machine learning technology achieves self-learning and adaptive functions of microarchitecture software. In the case analysis section, this paper demonstrates the application effect of network data mining in microsystems software through specific cases. These cases not only validate the effectiveness of network data mining in microarchitecture software design, but also provide useful references for other similar applications.

With the continuous accumulation of enterprise data and the continuous development of technology, data-driven decision-making has become an important trend in enterprise management. Human resource management, as an important component of enterprise management, also requires the use of data analysis tools to optimize the decision-making process. Traditional human resource management strategy algorithms are often based on experience and intuition, lacking scientific data support. These algorithms often struggle to achieve ideal results when dealing with large and complex human resource data. This study aims to provide more scientific and accurate data support for human resource management by introducing the K-means algorithm. The main research purpose of this paper is to detect and identify the data in enterprise human resource management. Data mining can realize the comprehensive detection and analysis of poetry, but the application of data mining algorithms in human resource management data processing is not much. This paper hopes to analyze the anomaly of human resource data statistics by data mining method. According to the characteristics of the K-means algorithm and DBSCAN algorithm, a hybrid K-means algorithm is proposed in this study, which can automatically generate a more appropriate K value and realize parameter self-adaptation with less manual intervention. The optimized clustering K-means algorithm obtains the initial value of DBSCAN through calculation, and then the abnormal data of human resources statistics can be obtained through the second clustering calculation. The optimized K-means algorithm can detect human resource statistics data in real time, and give a new idea for human resource analysis of data anomalies.

With the widespread application of modern information technologies such as big data, enterprises have generated a large amount of data in their daily operations. The traditional audit methods have drawbacks such as high cost and high consumption, and can no longer meet the needs of audit work in the digital era. Therefore, it is urgent to adopt audit methods suitable for the digital era to improve audit quality and reduce enterprise Audit risk. For professional auditors in enterprises, it is very important to utilize emerging technologies such as data mining algorithms. The audited enterprise may tamper with its financial statements, and identifying high-quality audit data from massive amounts of data is a huge challenge. Compared to traditional audit methods, data mining algorithms can significantly improve the efficiency and accuracy of audit work. For example, the BP neural network, with its powerful nonlinear mapping ability, can capture complex relationships in data; Support vector machines classify data by finding the optimal hyperplane in high-dimensional space and have good generalization ability; random forest reduces overfitting and improves prediction accuracy by integrating multiple decision trees; Association rule algorithms can discover interesting relationships between data items, helping auditors identify potential fraudulent behavior or abnormal transactions. Therefore, the purpose of this study is to accurately evaluate and reduce the audit risk of enterprises, and to build an audit risk model using computer data mining algorithms. This provides necessary reference and guidance for auditors to conduct data analysis and mine valuable data during the audit process of enterprises, thereby improving audit efficiency.

Residents’ healthcare consumption involves many aspects such as residents’ expectations, medical institutions’ reputation, and residents’ trust, resulting in a large difference between the evaluation results and the actual value, and low residents’ satisfaction. Therefore, a method for evaluating residents’ satisfaction with healthcare consumption based on data mining and knowledge mapping is proposed. Under the framework and connotation of evaluation indicators, the evaluation indicator system of residents’ satisfaction with healthcare consumption is constructed to obtain evaluation indicators; the AHP method was used to calculate the weight of the above indicators of residents’ satisfaction with healthcare consumption; combined with the index weight, the improved hierarchical clustering method in data mining is used to cluster the evaluation indexes of residents’ satisfaction with healthcare consumption; according to the clustering results, an evaluation model was built through the knowledge map to evaluate the residents’ satisfaction with healthcare consumption. The experimental results show that the evaluation value of residents’ satisfaction with healthcare consumption is basically consistent with the actual value, and residents’ satisfaction is high.

In the context of globalization, the importance of international communication has become increasingly prominent. However, in the face of massive international communication information, how to effectively select and formulate communication strategies has become a big challenge. The traditional manual screening method is not only time-consuming and labor-intensive but also easy to be affected by subjective factors. This study aims to address the efficiency and accuracy issues in current international communication information screening and dissemination strategy formulation through the application of deep learning algorithms. It is of great theoretical and practical value to use deep learning algorithms to conduct intelligent screening of international communication information and research on national and regional communication strategies. To explore the application of deep learning algorithms in the intelligent screening of international communication information and the research of national and regional communication strategies, first, we will introduce the basic principles and methods of deep learning, as well as its applications in natural language processing (NLP), image recognition, and other fields. Then, we will explore how to use deep learning algorithms for intelligent screening of international dissemination information, including information classification, clustering, topic modeling, etc. Combined with practical cases, the application of deep learning algorithms in national and regional communication strategies will be studied, including communication content generation, communication channel selection, and communication effect evaluation.

This paper addresses the critical and complex task of integrating music education information resources, highlighting the scattered nature and inefficient utilization of current resources in the field. The significant challenge of effectively consolidating diverse types of music education resources, such as digital audio, scores, and instructional videos, is addressed. We propose a novel algorithm, rooted in data mining techniques, specifically designed for the rapid integration of these resources. Our method involves a systematic approach that begins with standardizing the format of the input data, including audio lengths and image dimensions, to ensure uniformity. We then employ Convolutional Neural Network technology to extract features from audio, images, and videos, harnessing the power of deep learning to handle the multi-modality of the data. The extracted features from these varied sources are integrated into a unified format for subsequent processing. Following the feature extraction and integration, we utilize spectral clustering to categorize the music education resources. This clustering method is particularly effective in dealing with the complexities and nuances of the multi-modal data. Our experimental results demonstrate the efficacy of our algorithm in accurately classifying and integrating diverse music education resources, offering a promising solution to the challenges currently faced in the field.

In response to the uniqueness of sports learning evaluation, we innovatively chose neural networks as the modeling basis and constructed a sports teaching effectiveness evaluation model based on embedded neural networks. This model achieves intelligent and automated evaluation of physical education teaching through the flexible processing capability of an efficient learning ability logic system embedded with neural networks. Specifically, embedded neural networks can deeply mine complex data during the teaching process. The embedded neural network effectively solves the uncertainty in the evaluation process and significantly improves the accuracy and adaptability of the evaluation system. In the process of model construction, we pay special attention to the adaptive processing capability of data. By introducing an adaptive neural system, the model can dynamically adjust parameters to better adapt to different teaching scenarios and individual differences among students, ensuring the objectivity and impartiality of evaluation results. This adaptability not only enhances the flexibility of the evaluation system, but also provides solid support for its application in practical teaching.

Data mining technology can solve the hidden rules of data when solving problems, which has great advantages. With the increasing maturity of data mining technology, its application in teaching is also more and more. This paper aims to analyze the specific impact of campus football activities on students’ mental health quality through the application of data mining algorithms, especially decision trees and association rule algorithms. On this basis, a data mining algorithm is applied to study the influence of campus football activities on students’ sports quality and mental health state. First, the present situation of campus football is expounded. First, the development status of campus football is described, and the research of data mining algorithms is summarized. This paper establishes an analysis model of the influence of campus football on students’ mental quality and uses a decision tree and association rule algorithm in a data mining algorithm to analyze students’ sports quality and mental health state. The algorithm in this article adopts the method of one-time scanning of the database, and after generating new frequent itemsets, the database continuously decreases. When the pruning severity is set to 30–60, higher accuracy can be achieved, and the pruning severity is set to 40 in the design. Subsequent operations do not require scanning again, which can occupy less space and reduce time complexity. The algorithm test shows that the design accuracy and accuracy of the algorithm have been improved, which can meet the needs. The designed analysis of students’ sports quality and mental health state based on the data mining algorithm can provide effective data for the decision-making of campus football activities.

Technological developments in the field of education have contributed to the current surge in popularity of online courses. Every day, there is an exponential increase in both the rate of development and the availability of learning information. The trend in education systems across the world is toward putting the student at the center of everything. Education systems throughout the world are shifting toward a model that is more tailored to each individual student. This allows current technology to adapt different qualities of humans. Finding an appropriate learning strategy for a course with a large body of material that may be quite a challenge. The recommendation of a learning route aids students in methodically completing their coursework and reaching their objectives. Smart robots and computers are now able to comprehend individual-specific demands, technology advancements like AI, Machine Learning, and Big Data have made this possible. This paper suggests an AI-based learning-teaching model (AI-L-TM) for recommending learning paths that centers on analyzing learning performance and acquiring new information. Educational analytics improves a plethora of English-language individualized learning experiences by evaluating supplied data to provide valuable learning results. Through the use of Internet of Things (IoT) devices, data mining methods, and classroom data gathering, this project seeks to enhance the English learning experiences of college and university students. Here, AI methods might be helpful for a number of reasons, such as creating a learning–teaching model that mimics human thinking and decision-making and reducing uncertainty to make the process more efficient. Using artificial intelligence techniques for adaptive educational systems within e-learning, this paper presents a range of topics related to the field. It discusses the pros and cons of these techniques, and how important they are for creating smarter and more adaptive environments for online learning.

This study endeavors to introduce a method for measuring stock market investment risk, leveraging data mining techniques alongside decision trees (DTs). By harnessing extensive stock market data and integrating steps such as data cleaning, feature selection, and model construction within data mining technology, an effective risk measurement model is formulated. Specifically, DTs serve as the primary modeling tool, adept at capturing intricate relationships and nonlinear characteristics prevalent within the stock market, thereby facilitating precise measurement of investment risks. Through empirical analysis, the efficacy and viability of the proposed method in risk measurement are substantiated, furnishing investors with a pivotal decision-making reference. Overall, this study contributes to the ongoing discourse on stock market risk assessment by integrating advanced data mining methodologies, thereby enhancing the accuracy and reliability of risk evaluation in investment decision-making processes.

The creation of a personalized ideological and political education resource suggestion system has significant practical implications for boosting the efficiency of ideological and political education. Many challenges are preventing ideological and political education from progressing smoothly in the online environment, and the mainstream values of education are being affected as an effect. The educational model is reversed, there is a single way to communicate, and the educational content is solidified. It constructs a personalized recommendation user model for educational resources, and then builds a recommendation collaborative filtering algorithm using Data Mining (DM), that produces personalized suggestion of ideological and political education resources, improving the Collaborative Filtering Algorithm (CFA) allows for personalized recommendations of teaching materials for ideological and political education. As a result, DM-CFA has become a tool for education professionals can gain a better understanding of the learning and life characteristics of current college students through the collaborative operate of ideological and political education entities in the network environment, that likewise offers a diverse range of educational materials and varieties. As a result of the curriculum’s ideological function in mounding students’ brains, the ruling class’s history, gender, religion and nationality develop in society. Developing various learning channels for pupils can increase their interest for ideological and political research and help students grow holistically.

With the intensification of the competition for public medical resources and the rapid development of smart medical technology, the demand for in-depth analysis of athletes’ basic information and decision support is growing day by day. However, the practice of applying smart medicine to athletes’ physical information management is still relatively limited. This paper aims to explore how to use smart medical decision technology to build an integrated athlete physical information management and decision support platform. Taking football players as the research object, this paper integrates data mining technology into a smart medical decision-making model, to achieve accurate analysis and effective management of athletes’ physical fitness information. The core of the research is to develop a smart healthcare decision model that can identify key information and patterns on the platform through data mining technology. In the selection of the decision algorithm, this paper adopts cluster analysis and association rule mining algorithms, which can deeply dig into the potential rules and correlations in the data. Through the application of an algorithm, we can accurately evaluate the physical condition of football players, and provide scientific guidance and suggestions for their subsequent physical training. The algorithm in this study is not only innovative in theory, but also shows good adaptability and effectiveness in practical application.

In the rapidly evolving domain of vocational education, the deluge of digital teaching resources necessitates an advanced personalized recommendation system to optimize learning experiences. Traditional recommendation algorithms struggle to grasp the dynamic nature of user interests and the intricate web of interactions between users and resources. Therefore, in this paper, an efficient approach combining dynamic user interest modeling with a graph-based recommendation system is proposed. Transformer architectures are utilized to capture temporal shifts in user preferences through feature engineering of browsing behaviors, click history, and preference settings. A graph neural network is employed to capture the complex relationships within a graph-based framework. It uses graph convolutional or attention networks to dynamically assign weights that reflect the influence of neighboring nodes. Experiments are conducted on real-world datasets to assess the performance of the proposed approach in terms of precision, recall, and $F$1-score compared to traditional systems. The proposed approach provides more accurate and personalized teaching resource recommendations in vocational education settings.

In digital platforms, abnormal events involve multiple data sources and complex information types, and the difficulty of tracking them increases due to the similarity and interaction between components, operations, and user behavior. Therefore, in order to achieve precise tracking and efficient processing of abnormal events, and thereby improve the stability and security of the platform, a digital platform abnormal event tracking method based on a knowledge graph is proposed. First, using data mining and association rule techniques, abnormal event data in the digital platform are effectively collected and integrated. Subsequently, the data are input into a model that integrates residual atrous convolutional neural networks and conditional random fields to achieve precise identification of key entities. On the basis of entity recognition, the correlation between entities is extracted and a knowledge graph architecture for abnormal events is constructed, providing a solid foundation for subsequent deep analysis. Through a visual interface, the knowledge graph of abnormal events can be intuitively displayed, making it easy for users to quickly understand the full picture of the event. At the same time, the knowledge graph subgraph matching algorithm is adopted, combined with flow graph indexing and optimal matching sequence, to achieve accurate tracking and recognition of abnormal events. The experimental results show that this method can effectively track abnormal events in the digital platform. The first detection time is relatively short, with a mishandling time of 8.3s and data duplication of 7.9s. The continuous tracking time is long, with a security vulnerability of 50min. The false alarm rate is low, with the highest being 2.1% for data duplication, and the false miss rate is also low, with the highest being 0.8% for mishandling. This method can identify the number of abnormal events, which helps to understand the stability and health status of the platform. By timely and effectively preventing abnormal events, the frequency of their occurrence can be reduced, and the overall security and stability of the platform can be improved.

In order to evaluate the quality of online teaching in higher vocational colleges quantitatively under the theory of constructivism learning context, a quality management model of online teaching in higher vocational colleges based on constructivism learning context theory and data mining algorithm was proposed. First, a fitting parameter analysis model of online teaching quality in higher vocational colleges under this theory is constructed, and then the fitting benefit parameters are extracted by combining the reliability index parameter analysis with the AMOS model based on the index parameters of acting constructively, social interaction and context, and using advanced thinking data analysis methods, such as inference, analysis and identification. Then, according to the characteristic elements of online teaching participants in higher vocational colleges to complete tasks and achieve goals, the fuzzy C-means clustering big data mining algorithm is adopted to carry out quantitative analysis and characteristic elements analysis in the online teaching quality assessment process under the constructivism learning context theory, so as to determine the internal composition of online teaching quality model in higher vocational colleges, that is, online teaching quality elements. These quality factors are then coded in the first and second layers using knowledge transfer methods and rough set concept analysis methods. At the same time, the process characteristic parameters of all factors were analyzed, and the concepts were classified according to the big data mining model. Then, the theoretical parameter analysis model of online teaching quality in higher vocational colleges is constructed to realize the evaluation and quantitative analysis of online teaching quality in higher vocational colleges under the constructivism learning situation theory. Finally, factor analysis and reliability test are achieved by using KMO and Bartlett tests. The results of simulation analysis show that the reliability and accuracy of online teaching quality management evaluation in higher vocational colleges are good, and the credibility level is high under the constructivism learning situation theory.

This study employs LendingClub data in the field of personal credit risk control as an illustrative case. Various data mining models, and support vector machine, are utilized for training purposes. Additionally, a Stacking model is integrated into the analysis to forecast customer default likelihood. Subsequently, lending decisions are made in accordance with these predictions. The outcomes indicate a reduction in customer default rates compared to scenarios without the application of data mining models, thereby achieving our goal of risk control.