Student sports data analysis and physical fitness evaluation based on convolutional neural networks

Abstract

In order to achieve the analysis of student sports data and physical fitness evaluation, the author proposes a method based on convolutional neural networks. A hybrid algorithm combining genetic algorithm and error backpropagation algorithm (BP) is used to train convolutional neural networks. The algorithm first uses genetic algorithm for global training, and then uses BP algorithm for local precise training. This overcomes the drawbacks of traditional BP networks such as long training time and frequent local atmospheric drift, and improves global circulation performance. A neural network model was established to display the relationship between the total physical activity score and multiple test scores of high school students by utilizing electrical networks to demonstrate the connectivity of the neural network. This model aims to evaluate the athletic performance of college students and compare the results with other experimental models. The results indicate that the neural network-based model for evaluating college student physical activity can reflect the differences in physical activity and scores among all students, making it a suitable standard for evaluating high school student physical activity. The fitting accuracy of deterministic neural network models is higher than that of multiple linear regression models, which means that neural network models better reflect the performance of the network. The accuracy of various indicators of student physical fitness and total score makes the model easy to operate, accurate to predict, and effective analysis is scientifically reasonable.