Modeling Social Network of Professional Sports Athletes Based on Machine Learning Algorithms.

With the progress of society and the development of science, the competition of competitive sports has become extremely fierce, and the talents of competitive sports have gradually shown their strong vitality. Elite athletes are an important group for the development of China's competitive sports industry. To continue to progress and develop, it is inseparable from the careful training of elite athletes. In order to closely integrate the needs of China's socialist modernization and the training of elite athletes, sports schools that focus on cultivating elite athletes need to strengthen their sense of urgency and mission. It is necessary to meet new challenges at the height of social development and constantly optimize the construction of the curriculum system and the reform of the teaching operation mechanism. The discussion and research of the current elite athlete's talent training model has become a real problem faced by sports colleges, education departments, and sports departments. This paper conducts an experiment on the modeling of the sports athlete's social network based on machine learning, and the experimental data are shown as follows: among the athlete social network pictures optimized by machine learning, there are 1,200 male athletes, accounting for 48%, and 1,000 female athletes, accounting for 40%. Before the optimization, male athletes accounted for 70.58% and female athletes accounted for 16.3%, which was a decrease of 22.58% for male athletes and an increase of 23.7% for female athletes. From the above data, it can be seen that after the research on the social network of sports athletes through machine learning, it has a positive effect on the development of the social network of athletes. [ABSTRACT FROM AUTHOR]