Aiming at the disadvantages of large amount of calculation and low time efficiency of traditional maximum inter class difference method (OSTU) in image segmentation, a sparrow optimized OSTU segmentation method (SRWSSA) based on singer chaotic map and random walk strategy is proposed. Firstly, singer chaotic map is used to improve the initialization sparrow population, increase the diversity of initialization population and improve the global search ability; Secondly, the random walk strategy is used to perturb and mutate the updated optimal sparrow, so as to further increase the population diversity and enhance the local search ability; Finally, the standard image is segmented by two-dimensional OSTU using the proposed optimization algorithm to obtain the optimal threshold segmentation image. The SRWSSA algorithm proposed in this paper has significantly improved the optimization ability and iteration time. Compared with PSO-OSTU and SSA-OSTU, the number of iterations is reduced by 83.3% and 76% respectively. The image peak signal-to-noise ratio is increased by 8.2% and 11.3% respectively, and the running time is also improved. Practice shows that this method is feasible.