Application of Intelligent Algorithm in Table Tennis Training Data Analysis and Personalized Guidance

With the improvement of the competitive level of table tennis, traditional training methods have been difficult to meet the needs of precision and personalization. By introducing intelligent algorithm, this study collects and analyzes the training data of table tennis players, including hitting speed, hitting angle, moving trajectory, reaction time and hitting success rate, so as to reveal the technical characteristics and potential problems of players. Using the Long Short Term Memory (LSTM) model to analyze time series data, the model can accurately predict the action sequence of athletes and provide personalized training guidance based on this. The research results show that the intelligent algorithm can effectively identify the advantages and disadvantages of athletes in technology and tactics, and provide targeted improvement suggestions for coaches and athletes. Personalized guidance involves technical action optimization, reaction speed improvement, tactical adjustment and physical and strength training, aiming at improving athletes' competitive performance. Through practical application, this study has confirmed the effectiveness and practicability of intelligent algorithm in table tennis training data analysis and personalized guidance, and provided new ideas and methods for the scientific training and long-term development of table tennis.