The search ability of the sparrow search algorithm is easy to decline due to insufficient diversity of the initialization population, and the algorithm is easy to fall into local optimal in the late search period. In view of these problems, a multi-strategy fusion sparrow search algorithm (ISSA) is proposed. Specifically, the high-dimensional Sine chaotic mapping is introduced to initialize the population in the algorithm’s initialization stage, so as to improve the quality of the initial population and enhance the diversity of the population. Then, the attenuation factor is introduced in the discoverer stage, and the adaptability of the attenuation factor balances the performance of the early global search and the later local optimization. Finally, the Cauchy mutation and change selection strategy are introduced so that the searching individual can jump out of the local limit to continue the search and enhance the local search ability. Six benchmark test functions are randomly selected, and the experimental results verify that ISSA has been effectively improved compared with the original algorithm in terms of finding the optimal value.