Deep neural nets for Evaluation method of Table tennis athletes' psychological load intensity based on Artificial Intelligence

Abstract

Abstract Table tennis athletes are easily disturbed by the environment, psychology and other factors in the process of competition, and there is high uncertainty. Therefore, an artificial intelligence based evaluation method of Table tennis athletes' psychological load intensity is proposed. Based on the collection and monitoring of Table tennis athletes' psychological load data, this paper preprocesses them to complete the monitoring of Table tennis athletes' psychological load intensity. Based on this, from the factors that affect the psychological load of Table tennis athletes, this paper constructs an evaluation index system of Table tennis athletes' psychological load intensity including training pressure and environment. According to the index system, the psychological load intensity of Table tennis athletes is evaluated by fuzzy mathematical tools. The results show that the psychological load of the experimental Table tennis athletes is 6.45, which belongs to moderate load; Moreover, the evaluation method has a high accuracy in evaluating 8 items of Table tennis athletes' psychological load intensity, such as too long training time, too much training content and noise impact, which can effectively improve the training quality of Table tennis athletes.