Abstract: Aiming at the problems that the diversity of the original sparrow search algorithm is reduced, it is difficult to jump out of the local optimization, and the convergence accuracy is not enough, a multi strategy optimization sparrow algorithm based on chaos is proposed. Firstly, the population is initialized by circle chaotic map to generate a more evenly distributed sparrow population and increase the diversity of the population; Secondly, the adaptive proportion is introduced to dynamically change the proportion of the population size of the discoverer to the total population size, so as to balance the global search and local mining ability of the algorithm; Then Levy flight is introduced to improve the location update method of the discoverer, improve the search range and local search ability of the algorithm, and accelerate the speed of convergence to the optimal value; Finally, the fusion of dimensional mutation and reverse learning is selected to disturb the current global optimal position, and the optimal value before and after mutation is selected as the current global optimal value by greedy algorithm, so as to improve the ability of the algorithm to jump away from the local optimal value. This time, 12 benchmark functions and Wilcoxon rank sum test are selected for verification, and compared with six other algorithms, which proves that the above strategies can significantly improve the performance of the algorithm. At the same time, the improved algorithm is applied to engineering practice. This paper selects compression spring design optimization problem to verify the feasibility and superiority of the proposed improved algorithm in engineering design.