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Aiming at the manipulator is affected by uncertainties such as internal friction and time-varying disturbance, the tracking accuracy of its trajectory tracking control system will decrease and affect the stability of the system, an adaptive control method based on radial basis function (RBF) neural network is proposed. Firstly, the RBF neural network is used to identify the dynamic model of the manipulator by offline training and online learning. Secondly, the RBF neural network adaptive control algorithm is designed to approach the friction in the manipulator control system to obtain the compensation control. The robust term is designed for time-varying disturbance and neural network approximation error to overcome the influence of many uncertain factors. At the same time, the Lyapunov function is constructed to analyze the stability of the designed control system. Finally, simulation results show that the proposed control method has higher tracking accuracy, anti-interference ability and stronger robustness.