Sparrow Search Algorithm (SSA) has better performance than other biomimetic algorithms. However,its convergence accuracy is not high enough,and it is prone to fall into local optimum under complex multi-modal functions. In order to overcome these defects,this paper proposes an improved multi-strategy SSA based on K-means. The algorithm adopts multiple search strategies. Firstly,the initial population is clustered by K-means to accelerate information exchange within the population. Then,the sine and cosine search strategy is used to update the position of the followers,and the adaptive local search strategy is used to update the optimal individual,so as to find a more reliable feasible solution and improve convergence accuracy and optimization ability. The dual-strategy SSA is compared with the two single-strategy SSAs through 10 test functions,and it is verified that the introduction of the strategies can effectively improve the optimization ability of SSA. The two single-strategy SSAs have stronger optimization ability,and the dual-strategy SSA has weaker optimization ability. Finally,the three SSAs are applied to the LQR control of active suspension.The experimental results show that the optimization effect of the dual-strategy SSA is not ideal,while the two single-strategy SSAs have significant optimization effect,which can improve the performance of active suspension.