The safety, comfort, and energy feedback of active suspension in a single control mode is mutually restricted. To meet the needs of drivers and passengers for vehicle driving performance under different road conditions, this paper proposes a multi-mode switching control strategy of an intelligent suspension system, aiming at improving the stability and comfort of vehicles under different road conditions. In this paper, the adaptive Kalman filter algorithm with a forgetting factor is used to estimate the road input. The accuracy of the algorithm in estimating the road input is verified by simulation and experiment. The single-double threshold logic judgment method is used to formulate the switching rules between each working mode. In the controller, the PID control of the BP neural network and the LQR control optimized by GA are used to optimize and adjust the vehicle driving performance indexes in different modes, which effectively solves the problem of limited adaptability of suspension control optimization objectives under different road driving conditions. Based on the data from the vehicle acceleration sensor and road condition sensor, the modeling and simulation of the switching control system are carried out. The simulation results show that the designed control system can effectively improve the comprehensive performance of the vehicle under different road driving conditions. Compared with the traditional active suspension, the body acceleration is increased by 26% on the B-grade road surface, which effectively improves the user’s ride comfort. Compared with the traditional active suspension, the tire dynamic displacement is increased by 24% on the D-grade road, which can significantly improve the overall performance of the vehicle and meet the design requirements of the system.