Globally, it is widely accepted that the physical health of the young generation is continuously declining which remained unnoticed in many countries including China. Therefore, it is very important to scientifically investigate the evaluation index system and effectively interrogate the current health position of the young generation especially students. The results regarding health issues will be further investigated in terms of finding the optimal solutions for improving the status quo. Considering adolescent physical health evaluation indicators of Chinese students, this paper introduces the new era of adolescent physical health evaluation which should focus on “health quality indicators” for analyzing the objectivity and applicability of the indicators through testing and research methods. The method of simulation experiment research is used to demonstrate the scientificity and validity of the index. The optimization of the physical health evaluation indicators of adolescent students helps in assessing the health quality of adolescents, scientifically and accurately. In addition, it can effectively improve the physical health problems faced by adolescents. The proposed model achieves the evaluation indicators of adolescent health quality, collects physical health information and exercise data of adolescent students in a certain area of the northwest through big data, and conducts research as the research object. Furthermore, through the collation and analysis of the correlation data of the aerobic capacity evaluation indicators, the results show that the mid-run test value currently used in China is negatively correlated with the relative value of the measured maximum oxygen uptake on the treadmill.
Sports have gradually gained popularity, and the risks associated with them have risen as well. In today’s world, college students are a diverse population, and sports are very popular among them. The construction of sports risk assessment system in ordinary colleges is significant to improve physical quality of college students and ensure safety. College sports accidents have occurred on occasion in recent years, causing not only enormous pain to students and parents, but also casting a dark shadow over the sport. This work takes the risk analysis of college students’ sports as the background, and uses a data-driven neural network to conduct knowledge discovery of college sports threats. It evaluates the sports risks of college students’ physical education, and builds an index system of sports risk assessment in ordinary colleges and universities, which provides a certain basis for avoiding and reducing sports risks. This work studies an end-to-end one-dimensional convolutional neural network algorithm for risk assessment of college sports. In order to extract complementary structures in different scales, a multi-scale fusion framework is constructed using convolution kernels of diverse sizes. In this paper, the residual network structure is introduced to deepen and improve the network, and an attention module suitable for one-dimensional residual network is designed. It is embedded into the residual module to construct a multi-scale attention residual network (MSAR) model. Finally, validity and superiority of proposed model are verified by experimental data, which can effectively evaluate the sports risk of college students.
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