In order to solve the problem of inaccuracy and poor real-time performance in the inverse solution method of series manipulator based on neural network, the particle swarm optimization algorithm is used to optimize the structure of radial basis function neural network. A manipulator inverse kinematics algorithm based on PSO-RBF neural network is proposed. Firstly, the neural network training and test parameter samples are obtained by the forward kinematics model. The Euler angle transform is used to establish the manipulator joint pose mapping relationship at the input of the neural network. Then the PSO algorithm is used to optimize the parameters of the radial basis kernel function and the test sample is solved and analyzed. Finally, the motion trajectory of the manipulator solved by inverse kinematics is obtained and the reliability of the algorithm is verified. The simulation results show that the inverse kinematics algorithm based on PSO-RBF neural network can quickly obtain the joint angle that meets the accuracy requirements, which provides theoretical support for further mechanical control of the manipulator.