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This paper presents an adaptive H2/H∞ control strategy for a semi-active suspension system with unknown suspension parameters. The proposed strategy takes into account the damping force characteristics of continuous damping control (CDC) damper. Initially, the external characteristics of CDC damper were measured, and a forward model and a back propagation (BP) neural network inverse model of CDC damper were proposed using the measured data. Subsequently, a seven-degree-of-freedom vehicle with semi-active suspension system and H2/H∞ controller was designed. Multiple feedback control matrices corresponding to different sprung mass parameter values were determined by analyzing time and frequency domain performance. Finally, a dual observer system combining suspension state and parameter estimation based on the Kalman filter algorithm was established.