Aiming at the problem that the performance of the stage multi-axis synchronous system cannot meet the time limit of the control task due to the degradation of the actuators， and the existing maintenance strategy is difficult to reach the optimization， this paper proposes a reinforcement learning-based predictive maintenance strategy for the stage multi-axis synchronous system. Firstly， reinforcement learning is introduced in a cascaded manner， and constructing a control architecture with capabilities for lifespan prediction and autonomous maintenance， which operates with different sampling rates. Secondly， focusing on the intervening maintenance strategy and the influence of multi-source uncertainty on the actuator degradation process， based on the algorithms of Kalman filtering， Expectation-Maximum， and Rauch-Tung-Striebel smoothing， by the real-time perception and estimation of actuator degradation state， and a daptive update of degradation model， the prediction accuracy of the remaining life of the multi-axis synchronous system is ensured. Combined with the real-time perception， deviation of remaining life prediction， and the actuator degradation state， the objective function of a Q-learning algorithm is constructed. The optimal adjustment of maintenance control is carried out through trials and errors to obtain the maximum life extension reward and realize intelligent optimization maintenance of the stage multi-axis synchronous system. Finally， the effectiveness of the proposed method is verified by simulation experiments of the stage multi-axis synchronous control system， improving the system maintenance efficiency.