The three-dimensional path planning problem of unmanned aerial vehicle (UAV) is a very complex global optimization problem. However, UAV path planning based on heuristic optimization algorithms has the problems of slow speed and insufficient accuracy. To solve this problem, a UAV path planning method that improves the dung beetle optimization algorithm is proposed. First, an improved dung beetle optimization algorithm (BCLDBO) is proposed by introducing Bernoulli chaos map, variable spiral search strategy, new inertia weight and Levy flight strategy. Through experimental comparison with other algorithms on six benchmark test functions, it is proved that the BCLDBO algorithm has higher optimization accuracy and faster convergence speed. Secondly, the path planning objective function is established through the track length cost, height cost, smoothing cost and threat cost, and three-dimensional mission spaces with different complexities are constructed. Finally, the BCLDBO algorithm is applied to the UAV threedimensional path planning problem, which proves that this algorithm has lower path cost and better path planning effect than other algorithms.