The traditional sparrow search algorithm (SSA) has the problems that it is easy to fall into the local optimum and the search ability is insufficient in the process of optimization.In order to solve the above problems,an improved sparrow search algorithm (ISSA) based on Gaussian cloud improvement is proposed.First,Bernoulli chaotic mapping is used to initialize the population to improve the initial population quality of the algorithm;secondly,an adaptive Gaussian cloud mutation strategy is introduced in the update of the finder position to improve the global search ability of the algorithm in the iterative process;finally,the reverse t distribution learning strategy is used to perform random reverse learning on the optimal position to improve the algorithm′s ability to jump out of the local optimum.In the simulation experiment,this algorithm is compared with other four basic algorithms with 13 benchmark functions,and compared with other ISSAs.The experimental results show that the algorithm has good convergence and accuracy,and the global exploration ability is greatly improved compared with the original algorithm.The ISSA is applied to the Kapur entropy multi-threshold image segmentation task,and the results show that ISSA has higher segmentation accuracy than the other four basic algorithms.