LAUSR

Due to the limitation of sports movement, the current simulation technology of sports entities is prone to deficiencies in capturing dynamic motion figures and is prone to lack of accuracy. It is also affected by external noise and brightness. To solve these problems, this paper proposes a sports entity simulation based on the fish swarm algorithm and compares the figure effectiveness, figure segmentation, core point, and noise reduction effect of the two in the shooting figure. Through the comparison, it is found that the figure is more appropriate to the real moving figure, the motion capture is more accurate, and the number of core points is related to the accuracy of motion capture. The more core points, the more accurate the motion capture, and the noise reduction effect is also increased by 20.3%, which reduces the impact of brightness on the motion simulation. The difference in the effect of the traditional simulation technology (particle swarm algorithm) and the entity simulation based on the fish swarm algorithm was also compared. The combination with the artificial fish swarm algorithm is to simulate the moving entity and learn from some reference data. By comparing the data between the two after the experiment, it is concluded that the fish swarm algorithm is more effective in the simulation of sports entities.