In order to improve the stability and comfort of vehicle driving, a model predictive control system based on radial basis function（RBF）neural network is proposed, and the effectiveness of the active suspension control system is verified through simulation. Create a seven degree of freedom vehicle active suspension diagram and define the dynamic equation of the vehicle active suspension. Constructing an active suspension model predictive control system, utilizing the RBF neural network structure to capture the complex dynamic characteristics of the vehicle’s active suspension system. Through learning and training a large amount of data, the active suspension model predictive control parameters can be quickly established, ultimately achieving precise control of the vehicle’s active suspension system. Simulate the body acceleration, suspension displacement, and tire displacement of the vehicle’s active suspension using Matlab software to evaluate the driving performance of the vehicle under different control strategies. The results show that under the excitation of road signals, using model predictive control, the body acceleration, suspension displacement, and tire displacement of the vehicle’s active suspension vary significantly. Using RBF neural network model predictive control, the vehicle’s active suspension has relatively small changes in body acceleration, suspension displacement, and tire displacement. The proposed RBF neural network model predictive control system can enhance the anti-interference ability of vehicle active suspension, thereby maintaining the stability and comfort of vehicle driving.