Rationality evaluation of batting action of table tennis players based on support vector machine model

Abstract

With the rapid development of Internet technology and the continuous progress of society, machine learning, an artificial intelligence science, plays an increasingly important role in social production, scientific research and daily life. As a classic algorithm in machine learning, support vector machine has developed rapidly based on its unique advantages in small sample, nonlinear and high-dimensional pattern recognition. Table tennis is a sport with fast speed, strong rotation and high requirements for landing. The five elements of "strength, speed, rotation, landing and arc" constitute the basis of table tennis. In the training of young people, basic skills training should be done solidly. Therefore, this paper conducts corresponding research and analysis on table tennis hitting action based on support vector machine model. This paper combines the support vector machine model to study and analyze it. After the research in this paper, it can be seen that the vector machine model can have a certain impact on the table tennis action, and the impact is as high as 67.45%. The research in this paper lays a foundation for future research on table tennis strokes.