Tracing Mechanism of Sports Competition Pressure Based on Backpropagation Neural Network

Abstract

Through the overall situation of athletes’ competition pressure, the pressure level of participating athletes can be understood and revealed. Analyzing the sources of stress and influencing factors of athletes can find measures to relieve and reduce stress and provide theoretical reference for the regulation of athletes’ competition pressure. Based on genetic algorithm and neural network theory, this paper proposes a method of tracing the sports competition pressure based on genetic algorithm backpropagation (BP) neural network to solve the problem that traditional neural network learning algorithm is slow and easy to fall into local minimum. There is no significant difference between male and female athletes in the level of competition pressure. Athletes have the same training methods and the same goals, and the competition pressure tends to be the same, with no obvious difference; athletes with different educational backgrounds have no significant differences in training, academics, sports injuries, interpersonal relationships, social expectations, and evaluations. Due to the particularity of the stage, the competition pressure of fourth-year undergraduate and third-year masters is significantly higher than that of other grades. The number of athletes participating in college table tennis tournaments has very significant differences in competition dimensions. There is significant difference in training and self-expectation dimension difference. The competition pressure of athletes who participated in the college table tennis championship for the first time was significantly higher than that of athletes who participated repeatedly. There were significant differences between athletes before and after adapting to the venue. Before adapting to the venue, the competition pressure of athletes is generally greater. After adapting to the venue, the competition pressure of athletes has been relieved.