Aiming at the problem that it is difficult to model and control the complex systems, this paper adopts a data-driven control strategy, uses the input and output data to establish a dynamic linearization model of the controlled system, and uses the sliding mode predictive control method to control the dynamic linearized model. In the method, the parameter estimation error of the system dynamic linearization process is regarded as generalized disturbance, and the strong robustness of the sliding mode predictive control reduces the influence of generalized disturbance on the system control, which effectively improves the control effect. An analysis is made to the stability of the control algorithm, and the stability and robustness of the system with external disturbances and time-varying parameters are verified by simulation. The results show that, compared with the existing algorithms of model-free adaptive control, model-free predictive control and model-free integral terminal sliding mode control, the proposed algorithm has better control quality.