Aiming at the issues of local optima trapping, inadequate global search capability, and low convergence accuracy in traditional intelligent bionic algorithms when solving three-dimensional path planning problems of Unmanned Aerial Vehicles (UAVs), a novel UAV path planning method based on improved Dung Beetle Optimization (DBO) is proposed. Firstly, a three-dimensional model of mountainous terrain is established using a mathematical model. And with consideration of the objective function and constraints of UAVs, the experimental environment is more consistent with actual scenarios. Secondly, a segmented linear chaotic mapping is introduced to initialize the population of the DBO, enhancing its global search capability. An adaptive nonlinear decreasing model is employed to dynamically adjust the number of rolling-ball dung beetles in the early and late stages of the algorithm, thereby improving the convergence speed. Finally, the spiral search strategy of the Whale Optimization Algorithm (WOA) is incorporated into the position updates of breeding and foraging dung beetles to strengthen the local search capability of the algorithm and improve convergence accuracy. The improved DBO is compared with other intelligent bionic algorithms for 3D path planning of UAV in simulation experiments. The results demonstrate that the improved algorithm achieves better performance in terms of path length, convergence speed with smoother paths, which verifies its effectiveness.