Aiming at the problems of lack of search ability and falling into local optimization encountered by the standard student psychology based optimization(SPBO)algorithm in solving the UAV path planning,a kind of three-dimensional path planning for UAVs with improved student psychology optimization algorithm is proposed.First,in order to enhance the local search ability of the UAV,artificial group division and hierarchical learning are introduced to update the students in the student mental optimization algorithm.Secondly,in order to solve the problem of UAV falling into local optimization,the mining search mechanism in honey badger algorithm(HBA)is borrowed to jump out of local search. Finally,the results of MATLAB simulation experiments show that the average path length of the improved student psychology based optimization algorithm(ISPBO)is reduced by 0.1725 km,the average cost is reduced by 1.94%and the standard deviation is reduced by 84.07%,which verifies that ISPBO has stronger optimization ability and better stability.