Studying the hitting technique of student tennis players is of great significance in improving the technical and tactical level of student tennis and promoting the popularization and scientificization of tennis. Since the performance of tennis hitting action recognition depends on the representation of the action, this study utilizes the Gaussian mixture distribution as a statistical distribution of human action data to provide a more accurate description of tennis hitting action. This paper presents the LM skeletal point coordinate fitting method to enhance the structure of the Gaussian mixture distribution-based Hidden Markov Model to capture the act of hitting the ball. In the end, the biomechanical properties of the tennis hitting technique were examined by analyzing the motion images of forehand and backhand hitting techniques of college tennis players. The mean values of the angle between the racket surface and the ground during the forehand stroke and the topspin stroke were 82.96° and 35.95°, respectively, and the speed of the grip point and wrist joint was faster than that of the flat stroke during the topspin stroke. The angles and velocities of the knee, hip, and ankle joints of the lower limbs were faster than those of the open backhand stroke in the closed backhand stroke. The angle and speed are smaller than that of the open backhand stroke, while the angle of the shoulder and elbow joints is larger than that of the open backhand stroke. The tennis action recognition model and algorithm designed in this paper can effectively acquire and analyze the technical characteristics of tennis hitting.