To improve the accuracy of manipulator path control, the wavelet neural network algorithm optimized by swarm intelligence was used to track the path of, so as to achieve accurate and effective control. Firstly, the dynamic structure of the two link manipulator was analyzed. And then the path control model of the manipulator based on wavelet neural network was established. The particle swarm optimization was constructed according to the state variables of the manipulator, and the main parameters of the stable wavelet neural network model were obtained by updating the particle position. In the simulation process, the number of hidden layer nodes M and the main parameters of particle swarm optimization speed weight ω were set by differentiation. Experiments showed that when M=12，ω=1.2, the optimal path tracking performance of the manipulator can be obtained, and the average angle error and displacement average error were the minimum. Compared with the path tracking of manipulator based on wavelet neural network, the tracking performance after particle swarm optimization was significantly improved.