A Continuous Deep Learning System Study of Tennis Player Health Information and Professional Input

Abstract

The health status of elite tennis players and the results of tennis matches are positively proportional under normal circumstances. The physical and psychological functions of tennis players directly affect the athletic ability of tennis players. With the improvement of people's living standards, people's attention to tennis has also increased. Tennis has received increasing attention in China, and the training of tennis players has become increasingly necessary. However, China is still using the traditional means of obtaining athletes' health information to evaluate athletes' health information. This has led to imperfect research into tennis players' health information and professional input systems. This makes the understanding of the health information of athletes incomplete and profound, and it affects the athletic ability of athletes. In this paper, deep learning and a two-factor model are added to tennis players' health information and professional input, and the feasibility of a deep learning system to comprehensively improve health information input is explored. The experimental results show that the application of the convolutional neural network method in the system improves the response speed to the physical fitness state of tennis players by 5%. This adds technical support for timely understanding of tennis players' physical health information and prevents players from making mistakes on the court due to physical reasons.