In order to improve the accuracy of the sliding mode control of the manipulator, the RBF neural network is used to complete the sliding mode control of the manipulator, and the hybrid leapfrog algorithm in the swarm intelligence algorithm is used to optimize the model parameters. In the process of manipulator sliding mode control and manipulator trajectory tracking, the weights and thresholds of RBF neural network are used as frog individuals of the leapfrog algorithm. The frog group is composed of randomly generated multiple weights and thresholds, and the frog group is grouped. The global optimal individual is obtained by continuous regrouping and intragroup iteration, and after the optimal weight and threshold are obtained, the optimal sliding mode control model of the manipulator is determined. The experimental results show that the sliding mode control of manipulator based on bionic swarm intelligence optimization RBF neural network has high tracking accuracy.