In order to improve the accuracy of the robot dynamics model, a low-speed motion nonlinear dynamics modeling method of industrial robot based on phase space reconstruction neural network is proposed. It is confirmed by the largest Lyapunov exponent of joint motor torque data in advance that the robot has chaotic characteristics at low-speed motion. Therefore, experimental data and chaos theory is used to analyze low-speed motion nonlinear dynamics, instead of separately considering each factor that may cause the robot's nonlinear dynamics. The phase space reconstruction parameters of each joint are determined by autocorrelation method and false nearest neighbor method. Through data preprocessing and analysis, some joint position derivatives related to the torque data change law are determined. After phase space reconstruction, these derivatives are chosen as the inputs of neural network. Experimental results show that the proposed method can better describe the robot's low-speed motion nonlinear dynamics, and has smaller errors compared with ordinary BP neural network in the case of single joint rotation.