In order to improve the control performance of the intelligent pneumatic control valve, this article was based on the modeling analysis of the pneumatic actuator, A optimal control strategy of control valve based on model parameter learning was proposed. Firstly, the dynamic model of the pneumatic actuator was established, and the five-step switch control algorithm was analyzed. Secondly, the control parameter self-learning strategy required for optimal control was designed based on the model. Finally, according to the control parameters obtained by parameter self-learning,the five-step control method was improved to give an optimized control strategy and implementation steps. The experimental results show that there is no obvious overshoot in the control process of the proposed optimal control algorithm, and the oscillation is significantly weakened. The control accuracy is significantly improved, and the adjustment time is shortened. The average adjustment time of small strokes is shortened by 38.1%, and the average error is reduced by 61.4%.The average adjustment time of large stroke is shortened by 38.7%, and the control accuracy is improved by 39.4%.