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The tennis match is full of variables, and the outcome is affected by many factors, such as serving skills, physical strength and mental state. Good psychological quality is conducive to the stable performance of athletes. It is important to study these factors to improve the value of tennis matches. Firstly, this problem first selects indicators from the data set to establish a comprehensive evaluation system, including multiple indicators such as average serve speed and ACE number. Then the principal component analysis method is used to determine the number of retained principal components and calculate the weight. Then, according to the weight and index data of principal components, the comprehensive evaluation score of players in each round is calculated. Finally, through the calculation of the model, the performance of the players in each round can be quantitatively assessed and presented in a visual way. Secondly, we tested the scoring sequence of the two players in the first 10 matches through the Ljung-Box test, and the results showed that the score sequence of player 1 was autocorrelated in each match, while the score sequence of player 2 was random in only one match. Therefore, the performance of supporting players is affected by a variety of factors and is not random. Further analysis shows that the first two principal components of the players have a high correlation with the total score, while the third principal component has a low correlation. Thirdly, this question analyzes the performance of two tennis players in the match through the principal component comprehensive evaluation model, divides their performance into strengths and weaknesses, and adds corresponding labels to the data set. Then, a variety of machine learning classification models are used to dig and distinguish the player status. In the model evaluation, the random forest model performed the best, which could accurately identify the "momentum" change of players. Finally, by analyzing the characteristic importance of the model, some suggestions are put forward, such as improving ball speed, increasing service score and reducing unforced errors, so as to improve players' match performance. Fourthly, this question is tested in three randomly selected matches based on the trained random forest model. The results show that the prediction results of the model are highly correlated with the actual competition situation, which proves its accuracy. In addition, based on the research results, the model promotion process suitable for different competition types is provided. Based on the results of the analysis, this study provides suggestions to help coaches take advantage of the role of players' momentum in the game. At the same time, it puts forward the measures to deal with various events that affect the course of tennis match.
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