In tennis matches, “momentum” describes the situation where players are inspired by positive factors during the game. This paper focuses on the quantification of momentum, the impact of momentum on match trends, and the optimisation of players’ match strategies based on momentum. In this research, the Markov chain is used to quantify the momentum, and the “momentum score” of players is obtained. The Eta and Spearman correlation coefficients are used to study the impact mechanism of momentum on match trends. The results show that, within a 95% confidence interval, momentum has a strong correlation with the results of the current game of players, but a weak correlation with the result of each current point. In addition, in this paper, we construct a match strategy optimisation model for players based on momentum scores. On the one hand, the entropy weight method and TOPSIS are combined to evaluate players’ performances. On the other hand, a BP neural network prediction model is established based on a multiple linear regression model with 13 indicators in 10 categories to predict match trends. According to the evaluation and prediction results, a series of strategy optimisation suggestions are put forward for players to cope with matches.