Exploration on Psychological Well-being Education of College Athletes

Abstract

The mental health education of college athletes is an important work, because the mental health status of athletes has a crucial impact on their performance and results. The physical and mental health of athletes is the basis of whether they can achieve excellent results continuously and stably. Mental health education can help athletes better manage their emotions, pressure and psychological state, and ensure their physical and mental health. In this article, the optimized deep learning (DL) algorithm is selected to build an athlete's psychological well-being assessment model based on DL, and the network is trained according to the topological structure of the network and the setting of its parameters, and its system structure and function are designed in detail to provide an efficient solution for university students to carry out intelligent pre-alarm. The simulation results show that the time-consuming of the psychological characteristics identification method in this article is significantly lower than that of the traditional method, and it can effectively extract the psychological characteristics of university athletes, shorten the identification time and have high operating efficiency. The recognition accuracy is improved by about 10% after using the Dropout method. Applying this method to psychological well-being assessment can provide theoretical and technical support for psychological well-being education, thus assisting teachers to carry out more targeted psychological well-being education in combination with the results of psychological well-being assessment of university students.