In order to enhance the convergence speed and accuracy of UAV path planning in complex environments, and to avoid falling into local optima, a novel three-dimensional UAV path planning method based on the improved spider wasp optimizer algorithm is proposed. This paper introduces an adaptive t-distribution disturbance mutation and cubic mapping strategy for updating the search stage positions within the traditional SWO algorithm, which helps to prevent premature convergence to local optima. Furthermore, a periodically random amplitude dynamic adjustment for the pursuit and escape phases is incorporated to assist the algorithm in escaping local optima. The spiral update mechanism and Levy flight strategy are combined to enhance the global optimization capability of the algorithm, thereby improving its convergence precision. Finally, the performance of the ISWO algorithm is validated through experiments on eight test functions, and simulation results indicate that the execution time of the ISWO algorithm in complex terrain environments is reduced by 26.86% compared to the traditional SWO algorithm, and by 13.80% to 28.27% compared to other algorithms such as CPO, COA, GOOSE, PSO, and GWO. Additionally, the minimum fitness value achieved by the ISWO algorithm is 49.76% lower than that of the traditional SWO algorithm, and at least 27.73% lower than that of other algorithms. Consequently, it is concluded that the proposed improved algorithm can efficiently obtain a shorter and safer path in complex terrain environments.