Prediction of Sports Performance Combined with Deep Learning Model and Analysis of Influencing Factors

Physical education class helps students develop whole-body and fine motor skills and improve their strength, balance, and cardiovascular health. Sports also provide students with numerous social, psychological, and emotional benefits, which in turn improve their learning status and academic achievements. Nowadays, sports achievements are becoming more and more important, and many schools pay more and more attention to the development of sports. In order to improve sports achievements and predict sports achievements, we can combine them with relevant deep learning models to analyze sports achievements from multiple angles and factors, so as to improve sports achievements and predict sports achievements according to relevant influencing factors. Combined with the experimental part in this paper, the gradient compression algorithms under the deep learning model are compared. According to the data, the compression ratio of the AdaComp algorithm model is 1%, the training time is 5839 s, the average accuracy rate is about 90.9%, and the average loss value is 0.324. The compression ratio of the ProbComm-LPAC algorithm model is 1%, the training time is 5505 s, the average accuracy rate is about 91.8%, and the average loss value is 0.271. The compression ratio of the LAQ algorithm model is approximately 1%, the training time is 5467 s, the average accuracy rate is about 90.8%, and the average loss value is 0.554. When the number of training rounds increases from 20 to 5000, the accuracy of the three algorithm models is on the rise, but the accuracy of ProbComp-LPAC model is higher among the three models. When the number of training rounds increases, the data set loss rate of the three models is declining, indicating that the more the training times, the higher the correct rate, the smaller the loss value, and the higher the efficiency. Through four dimensions related to the influence of sports achievements-interest in seeking knowledge, ability pursuit, altruistic orientation, and reputation acquisition, this paper studies the influencing factors of sports achievements. According to the research data, most people think that the interest in seeking knowledge accounts for a large proportion of the factors affecting sports performance, with an average of 38.6709, accounting for 32% in the four dimensions. Through the study of students' gender and origin, this paper explores the analysis of the four dimensions of sports performance. It is believed that interest in knowledge is the most important factor. The average values of the four dimensions are 48.98, 52.37, 48.12, and 51.34, respectively. In order to accurately predict sports achievements, the characteristics of sports achievements prediction are sorted, among which the maximum number of action exercises is 0.24, the average score of sports action tests is 0.16, the video viewing time is 0.13, the sign-in rate is 0.09, and the minimum homework completion rate is 0.03. Predicting sports achievements through these characteristics can improve the accuracy of prediction. When the number of features in sports performance prediction gradually increases, the accuracy of sports performance prediction is also increasing. When the number of features is 8 and 9, the prediction accuracy is about 0.64.