In order to solve the problems of slow convergence speed, easy falling into local optima, and weakened population diversity in solving high-dimensional complex optimization problems in the sparrow search algorithm (SSA), an improved sparrow search algorithm based on seagull optimization algorithm operator and whale optimization algorithm operator (SWSSA) was proposed. Firstly, an adaptive population proportion strategy was designed to enhance the diversity of the population during the iteration process. Secondly, incorporating the whale optimization algorithm bubble net predation strategy in the local search stage, the local search ability of the sparrow search algorithm and accelerates convergence speed was enchanced. Then, an improved seagull optimization algorithm operator was introduced at the follower position to reduce the probability of the algorithm falling into local optima. Finally, 12 high-dimensional benchmark test functions and 16 high-dimensional datasets from UCI websites were selected for simulation experiments to compare SWSSA with basic SSA, SSA variant versions, golden sine algorithm (GSA), butterfly optimization algorithm (BOA), slime mold algorithm (SMA), seagull optimization algorithm (SOA), and other improved algorithms proposed by scholars. . The results show that the algorithm proposed achieves an optimal convergence accuracy of 100% on 12 test functions, with the fastest convergence speed on about 95% of the test functions, the highest classification accuracy on 9 out of 16 datasets, and the lowest number of 6 optimal feature subsets. It can be seen that the proposed algorithm has certain advantages in handling high-dimensional function optimization and dataset feature selection problems.