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In modern educational environments, the cultivation of physical fitness and the design of embedded system software also require detailed analysis and optimization. In order to comprehensively understand and optimize this system, this paper adopts the method of embedding correlation analysis, which is similar to the in-depth exploration of the interaction between software and hardware in embedded system software design. By constructing an embedded correlation analysis model, this paper analyzes the impact of various factors on running performance, providing a scientific basis for teaching adjustments and training plans. In embedded system software design, this analysis model is also applicable as it can help us identify and optimize the interaction relationship between software and hardware, and improve the overall performance of the system. The research results show that the key factors affecting college students’ running performance include training frequency, psychological stress, and height. This discovery corresponds to the identification and optimization of key performance indicators in embedded system software design. In embedded system design, we also need to focus on and optimize the factors that have the greatest impact on system performance. The results of this study not only provide inspiration for physical education teaching in universities, but also provide a new perspective and tool for researchers in the field of embedded system software design. The effectiveness of embedded-related analysis models has been validated in both fields, demonstrating their practicality and effectiveness in analyzing complex system problems.

This paper proposes a deep learning-based method for path planning of college students’ morality and faith instruction. Using a questionnaire and sampling inspection, the teaching effect index test and automatic monitoring of morality and faith teaching data are conducted. The feasible teaching strategy index of college students’ morality and faith teaching is based on students’ deep learning needs and expectations for ideological and political teaching in colleges and universities. The actual effect and teaching effect of teaching reform and innovation serve as the basic monitoring coefficient. Using deep learning and dynamic optimization detection methods, a feature clustering model for data collection and deep and surface learning of morality and faith instruction for college students is developed. A deep learning model for teaching morality and faith to college students is developed using a significant feature analysis method. Implement multidimensional spatial path optimization and data fitting. Perform quantitative regression analysis of college students’ critical, creative, and higher-order thinking in the index data of morality and faith teaching strategies. Detect and extract index data on morality and faith teaching strategies for college students. The test results show that this method improves the practicability and originality of morality and faith instruction for college students by optimizing the course planning and index data of feasible teaching strategies.

Personalized training is necessary to meet individual differences in educational attainment, opinions, job choices, and confidence since college enrollment is growing increasingly varied. To put this into practice, it will be essential to gather and assess data from a range of students, use institutional data to perform resource-effective investigations, and take into account the career prospects of learners while developing accurate teaching methods. This research shows a novel emperor penguin search-assisted artificial neural network (EPS-ANN) approach for effectively predicting college students’ career inclinations. This work uses a dataset of student academic concerts for training the proposed technique. The dataset is first preprocessed to develop the features evaluation. To extract the significant features from the normalized data, linear discriminant analysis, LDA, is used. The suggested method is used to further process these important variables to get good predictive performance. The parameters of the ANN are improved by using the EPS optimization process. The suggested approach is tested on the Python platform and examined using several metrics. Additionally, a comparison of the suggested and current approaches is done. In summary, the EPS-ANN approach outperformed other techniques in forecasting the career preferences of college students.

In order to improve the prediction accuracy of college students’ Internet+mass entrepreneurship and innovation practice effect, a prediction method based on an evolutionary algorithm is proposed. By comprehensively considering the family, school, personal, and social backgrounds of college students, a comprehensive evaluation index is constructed, and an objective function is set to minimize the distance between the questionnaire weight and the ideal weight. To avoid the evolutionary algorithm getting stuck in local optima, a simulated annealing strategy is introduced to optimize the mutation process, enhancing the algorithm’s performance. Experimental verification shows that this method not only selects evaluation indicators accurately and has high prediction accuracy, but also has a fast solving speed. It has been successfully applied in multiple universities, providing an effective tool for evaluating and improving the effectiveness of college students’ entrepreneurship and innovation practices.

Artificial intelligence (AI) in healthcare has recently been promising using deep neural networks. It is indeed even been in clinical trials more and more, with positive outcomes. Deep learning is the process of using algorithms to train a neural network model using huge quantities of data to learn how to execute a given task and then make an accurate classification or prediction. Apart from physical health monitoring, such deep learning models can be used for the mental health evaluation of individuals. This study thus designs a deep learning-based mental health monitoring scheme (DL-MHMS) for college students. This model uses the most efficient convolutional neural network (CNN) to classify the mental health status as positive, negative, and normal using the EEG signals collected from college students. The simulation analysis achieves the highest classification accuracy and F1 scores of 97.54% and 98.35%, less sleeping disorder rate of 21.19%, low depression level of 18.11%, reduced suicide attention level of 28.14%, increasing personality development ratio of 97.52%, enhance self-esteem ratio of 98.42%, compared to existing models.

With the development of big data (BD) and Internet of things technology, college students, as an important talent resource in national construction, pay attention to their autonomous learning behavior. Based on the theory of BD and Internet of things, this paper studies the influencing factors of college students’ autonomous learning (CSAL) behavior. First, it introduces the definition, characteristics and existing problems of CSAL behavior, expounds the influencing factors of CSAL behavior, studies the application of BD and the Internet of things, and understands the situation of CSAL through questionnaires and interviews. Finally, the survey shows that more than half of the students surveyed believe that learning is to acquire skills so as to find better jobs and better material life in the future. On average, 25% of students graduate from university through study. On average, 18% of students have strong interest in their research field and hope to obtain professional skills and give full play to their talents. On average, 6% of students study to see their value. Freshmen are basically not absent from school, while the number of sophomores, juniors and seniors has reached 15% of the number of undergraduates. The situation will be more serious in class. The survey results show that 45% of undergraduate students have been absent from class, of which 30% are occasional absenteeism and the rest are frequent absenteeism, which accounts for 14% of the total number. Among the graduate students, 7.3% of the students have been absent from class, of which 6% are occasional absenteeism and the rest are frequent absenteeism, reaching 1.3% of the total. The main learning methods used by junior students are classroom notes and textbooks. With the improvement of grade, the proportion of students learning multimedia and online learning is higher and higher. These students’ learning strategies have changed from traditional learning to today’s autonomous learning. They have found their own solutions in the learning process, and their learning strategies have undergone qualitative changes. Whether undergraduate or graduate students, more than 50% of students prefer their own major when they study independently.

Using survey method, this study compares the relative importance of exposure to Chinese media, pro-China local media, pro-democracy local media, and new media (e.g., Weibo, Facebook) on the building of national identity among Macau college students. We argue that the effect of media exposure on national identity formation is not uniform, owing to the political leanings of the media and the platform on which the information is transmitted (new media vs. traditional media). We find that getting news about China on Facebook is the most important predictor of the formation of national identity among college youth in Macau, followed by getting news on Weibo and exposure to traditional Chinese media. Conversely, exposure to pro-democracy local media and frequent use of Facebook exert a negative effect on national identity building among college youth in Macau. Positive sentiment toward China and trust in the central government act as mediators and fully explain the relationship between exposure to traditional pro-China media and national identity but cannot explain the positive effect of exposure to new media on national identity formation.

College students majoring in preventive medicine are the future main task force in combating the COVID-19 pandemic. This cross-sectional study aimed to evaluate career perceptions and professional plans of these students after the COVID-19 pandemic in China. A total of 372 (response rate: 93%) participants completed the survey. We observed that after the admission, students reported better comprehension of the preventive medicine major and employment prospect (dependent t-test: $2.51\pm 1.01$ vs. $2.09\pm 0.71$, $P<0.001$). The overall career perceptions of undergraduate students majoring in preventive medicine were positive $(1.99\pm 0.59)$. Especially for junior (Grades 1 and 2) students, which were more willing to recommend preventive medicine major to prospective students compared with senior (Grades 3–5) students. Junior students were also more enthusiastic about learning professional knowledge, had a stronger belief that their employment prospective would become better, and were more willing to pursue a relevant career. More than three-quarters (287, 78.85%) of the students tended to pursue graduate education, and the majority (228, 62.47%) of them preferred public institutions as future employers. With the increasing society-wide recognition of the importance of public health in epidemic prevention and control, the confidence of undergraduate students majoring in preventive medicine has been improved.

In the modern Chinese teaching process, the expressive skills of students are the comprehensive reflection of their knowledge of the language theory and their ability to communicate effectively. Improving the expressive skills of college students is currently one of the most urgent problems in the teaching of modern Chinese. In this paper, the connotation of the college students’ expressive skills is briefly introduced. We conducted analysis of the factors that affect the expressive skills of college students is conducted and proposed several methods to strengthen their expressive abilities.

This paper analyzes the current situation of psychological health among college students, to understand their existing health statuses. This can set the basis for improved development and teaching of content for the sports curriculum for future reference. 600 male and 400 female grade 2015 college students from Lanzhou University of Technology were randomly selected to take part in the study. The study demonstrated that the students passed national standards of 4 index tests: Body mass index, vital capacity, sit and reach, 1000 meters (800 meters). However, the test results for the remaining 2 index tests of standing long jump and pull ups were lower than the national standards.