The aim of this study is to investigate the influence of psychological capital on college students’ entrepreneurial intentions. Through a combination of relevant analysis and linear regression, the primary focus is on exploring the relationship between psychological capital and its four dimensions with entrepreneurial intentions. Firstly, the items in the psychological capital questionnaire were revised to align more closely with entrepreneurial contexts. Subsequently, the average deviations and standard deviations of each dimension of psychological capital were analyzed. Then, the correlation between psychological capital and entrepreneurial intentions was examined to explore the extent of their relationship. Finally, regression analysis was conducted on both psychological capital and entrepreneurial intentions, and utilizing a recurrent neural network model, the covariant relationship between entrepreneurial psychological capital and intentions was explored. The results indicated that the average scores for entrepreneurial self-efficacy, optimism, hope, and resilience were 3.91, 4.27, 4.19, and 4.15, respectively. The average value of psychological capital was 4.13, indicating a moderately high level. The correlation analysis between psychological capital and entrepreneurial intentions yielded a result of 0.562, indicating a moderate degree of correlation. The correlation coefficients of the four dimensions with entrepreneurial intentions were 0.390, 0.494, 0.531, and 0.467, respectively. The standardized coefficients for psychological capital and its four dimensions were 0.564, 0.382, 0.510, 0.536, and 0.468, all of which were statistically significant. Overall, psychological capital exhibited better predictive power for entrepreneurial intentions than its individual dimensions. The results from the deep learning model similarly demonstrated the positive role of psychological capital in entrepreneurial intentions, though the influence of ideological and political education (IPE) factors was relatively weaker. In conclusion, both psychological capital and IPE have a promotive effect on entrepreneurial intentions. This study provides a reference for the accurate evaluation of college students’ entrepreneurial intentions.

This work aims to optimize the physical education (PE) teaching effect based on deep learning (DL) to cultivate high-level college students better. Firstly, the present situation of college teachers' teaching ability is surveyed to realize the deficiencies in teaching. Secondly, an optimization algorithm is proposed to improve the node splitting mode. This algorithm can solve the problem of single and similar node splitting modes in the Random Forest (RF) algorithm. The independent node splitting method Iterative Dichotomiser 3 and Classification and Regression Tree in the algorithm are recombined, and new splitting rules are obtained through adaptive parameter selection. Finally, the scheme designed is tested. The results suggest: The results suggest: (1) During the training of the proposed algorithm, although the loss curve at 4550 and 6800 points has a small crest, the error of the network loss function shows a downward trend and tends to be flat; (2) Compared with unoptimized Genetic Algorithm (GA) and Genetic Algorithm-Back Propagation (GA-BP), the proposed algorithm shows better performance both in terms of time consumption and accuracy (time consumption is less than 5.4 ms, and accuracy is more than 95 %). In a word, using the GA-BP-RF algorithm proposed to improve the PE teaching effect is feasible. The proposed model provides ideas for applying DL technology to improve teachers' teaching abilities.

With the awakening of people's health consciousness, the concept and practice of health promotion has become the main target of health policies throughout the world. In this study, the relationship between physical education and health promotion was examined. Art students from a university in Taoyuan were selected for research, and a total of 320 questionnaires were issued. Invalid and incomplete questionnaires were eliminated, with a total of 227 valid questionnaires. Finally, the LISREL (Linear Structural Relations) model was used to analyze the correlation between various factors and health promotion. The results of the model analysis show that in terms of basic fit, the three factors of physical education (course design, teaching content, and activity design) have a high correlation with the influence of physical education, reaching a significant level (t > 1.96, p < 0.05). In terms of influence on sports participation, the three factors (physiological factors, psychological factors, social factors) of sports participation reached a significant level (t > 1.96, p < 0.05). The two factors of health promotion (physiological health, and mental health and practical ideas) have a high correlation with the influence of health promotion, reaching a significant level (t > 1.96, p < 0.05). In terms of overall mode fit, the overall mode fit standard χ²/Df was 1.344, less than the standard value of 3 or less, and the RMR value was 0.007, indicating that the χ²/DF and RMR result standards were appropriate, and the chi-square value was very sensitive to the sample size. Therefore, there was a positive correlation among physical education, sports participation, and health promotion. In conclusion, physical activities can improve the human body's immune function, reduce the symptoms of chronic diseases, and positively promote health. The research result is important for emphasizing the benefit of sports to art students, and provides reference for improving the quality of school physical education, and the physical and mental health level of people in Taiwan.

Given the rising psychological challenges encountered by university students, there is an imperative to address the pressing need for enhancing their psychological capital. This study is to design an innovative multimedia system that seeks to offer comprehensive psychological support and promotion mechanisms for university students. This is achieved through the integrated use of various media forms. Multimedia system group counseling was employed to assess and enhance the psychological capital of college students. This study comprises two main components: first, an analysis of the application of multimedia technology in education, and second, an empirical investigation into college students’ psychological capital through a questionnaire survey. The findings reveal that the introduction of group counseling via a multimedia system significantly enhances the psychological capital of college students. This improvement in psychological capital positively impacts the well-being and mental states of students and contributes novel ideas to mental health education for college students. The effectiveness of the group counseling intervention scheme within the multimedia system is evident, suggesting its potential for widespread adoption. The utilization of multimedia systems in educational settings emphasizes the importance of positive psychology for students and contributes to cultivating a positive and healthy psychological state. This study serves as a valuable reference for enhancing the psychological capital of college students, focusing on aspects such as independent thinking, decision-making, and execution.

With the development and application of the Internet of Things, cloud computing, Artificial Intelligence, big data, and other information technologies, sports fitness has ushered in new development opportunities. Intelligent sports detection has emerged as the times require. This paper takes intelligent physical fitness as the research object, explores the application of digital and intelligent image processing in physical fitness, and provides a theoretical reference for popularizing and implementing intelligent physical fitness in life. Firstly, the treadmill is an example to study the current situation of fitness equipment, especially the intelligent digital treadmill, and the future development trend of fitness equipment. Secondly, the upper and lower computer control scheme is proposed. The upper computer control scheme is selected, and the industrial computer is analyzed. The motion model of the main control motor and the calculation model of the heart rate control scheme and calorie consumption are established. Finally, the software and algorithm design of the control system are summarized, and necessary debugging, experiment, and analysis are carried out. The results show that: (1) the treadmill industrial control system can correctly receive the data sent by the Automatic Voltage Regulator (AVR) and express it in the form of images, which completely matches the asynchronous communication data format. (2) The ultrasonic distance detection experiment proves that the distance precision between the operating platform and the human body is extremely high, and its digital intelligent treadmill can meet human perception and corresponding control requirements well. (3) The heart rate pulse of the human body can be collected based on digital technology. The waveform displayed in the image is relatively stable and complete.

The development of precise detection and tracking methods for high-speed moving targets, coupled with accurate matching under varying lighting conditions, represents a formidable challenge in the realm of badminton robot creation. To tackle these intricate issues, this work introduces a suite of innovative techniques within the domain of intelligent robot image processing, offering substantial impetus to the evolution of badminton robots. Primarily, the work designs a high-precision binocular stereo vision acquisition system to enhance the accurate reconstruction of the badminton shuttlecock’s trajectory in three-dimensional space, enabling real-time monitoring and in-depth analysis of shuttlecock motion. Subsequently, an inventive dynamic threshold Gaussian mixture model, underpinned by singular value decomposition, is introduced. This model dynamically adjusts the decision threshold based on background-foreground similarity while ensuring synchronized exposure between the left and right cameras and timely data retrieval. Lastly, the study addresses the impact of lighting fluctuations on target matching through a Retinex-based color histogram matching method, adapting color histogram templates according to varying lighting intensities. Experimental validation involves the utilization of an industrial digital camera with frame exposure to capture badminton images, subsequently transforming image processing into motion control to effectively realize the badminton robot’s hitting action. The experimental outcomes highlight the following achievements: (1) The designed badminton robot achieves a remarkable 100% recognition rate for the shuttlecock under normal conditions, featuring a standard deviation of θ below 0.05°, a position standard deviation of less than 18 millimeters in the x-axis direction, and less than 8 millimeters in the y-axis direction; (2) The proposed binocular stereo vision acquisition system exhibits superior precision, profoundly supporting shuttlecock trajectory analysis; (3) The Gaussian mixture model, incorporating dynamic threshold adjustment via singular value decomposition, excels in detecting delicate and diminutive moving targets, showcasing high-level detection accuracy. These experimental findings offer novel insights into the domain of intelligent real-time image processing technology for badminton robots anchored in the IoT (Internet of Things) machine vision. The implications reverberate through the further advancement of badminton robots, charting a novel technical trajectory that amalgamates intelligent real-time image processing with the IoT. Consequently, the amalgamation bolsters hitting accuracy and motion performance, cohering with the ongoing trajectory of robotics technology development, which emphasizes the integration of intelligence and autonomy across diverse facets of robotics. This alignment propels continuous progress throughout the entire expanse of robotics technology.