Abstract: LiDAR has advantages such as high accuracy, strong anti-interference ability, small size, and light weight, which has important application values in sports recognition and evaluation scenarios: the accuracy of basic sports movements is crucial for scoring, and promoting standardization of athlete movements, which is of great significance to improve the athlete movements, especially to directly improve the scoring rate. The intelligent scoring system not only can score the performance of athletes to reduce controversies over subjective scoring items such as diving and gymnastics, but also can improve their competitive levels by providing feedback on the quality of their movements. An intelligent automatic scoring system based on LiDAR point cloud was proposed, which used human body object detection network, human body key point recognition network, action classification network, and dynamic time warping sequence action similarity evaluation algorithm to determine the difference between sequence actions and standard actions and score them. The experimental results show that the system has the characteristics of automation, intelligence, and real-time, which has certain reference significance for the construction of autonomous training evaluation systems in the field of sports.