Aimed at the problems that population diversity is weakening and easy to fall into local optimization in the late iteration by sparrow search algorithm (SSA), a chaos sparrow search algorithm is proposed based on hierarchy and Brownian motion (CSSAHB). Firstly, chaotic map is introduced for adjusting the key parameters of SSA. Secondly, a hierarchy is introduced. The three best individuals of the parent population are used to update the position of the vigilantes, enhancing communication among individuals and increasing population diversity. The uniform step controlled by Brownian motion is used to enhance the exploration ability of the algorithm. When the algorithm is stagnant, the Brownian motion strategy is used to disturb the individual to urge the algorithm to get rid of the local optimum. Finally, the greedy strategy is used to retain the dominant individuals and effectively accelerate the convergence speed. 12 test functions are made by the simulation experiments. The results show that the chaotic maps can enhance effectively the performance of the algorithm, and make the iterative maps perform the best. The improved algorithm is stronger in local optimum avoidance ability, faster at convergence speed, and higher in convergence accuracy.