Aimed at mechatronic servo systems with model uncertainty, a control strategy based on multilayer neural network interference compensation was designed.Multi-layer neural network was used to estimate staterelated disturbances online to improve the compensation accuracy based on the model feedforward control input.Then combined with the robust integral of the sign of the error (RISE) feedback control method, the neural network approximation error and unestimated interference were processed by the robust gain of RISE so as to suppress the adverse effect of interference on servo performance.Based on Lyapunov stability theory, it was proved that the closedloop system of the proposed controller is semiglobally asymptotically stable, and all signals of the system were bounded.The simulation results show that the proposed control strategy has good interference suppression ability and the tracking accuracy of the mechatronic servo system can be significantly improved