<p>The planning of unmanned aerial vehicle (UAV) paths represents a crucial aspect of UAV technology. The primary goal is to ascertain the optimum non-collision route from the point of departure to the point of arrival, while satisfying various requirements including minimizing path length, addressing environmental complexity, and avoiding obstacles. However, traditional optimization methods and heuristic algorithms often encounter challenges in complex environments, such as the tendency to become trapped in local optimum and insufficient obstacle avoidance capabilities. To address these issues, a UAV path planning model that comprehensively considers factors including path length, collision between UAVs, threat levels, flight altitude, and path smoothness is constructed. A dung beetle optimization with multi-strategy fusion (DBOMF) algorithm is proposed. In DBOMF, position update strategy for rolling dung beetles based on the golden ratio coefficient combined with the sine function balances global exploration with local exploitation capabilities. Then, breeding dung beetles and small dung beetles position update mechanism based on spiral searching is presented, allowing individuals to investigate the solution space more extensively along spiral paths, thereby enhancing the algorithm's searching efficiency and robustness. What’s more, an age elimination mechanism is employed to enhance population diversity. Finally, a progressive scaling adjustment strategy is implemented to dynamically balance random perturbations and optimum solution perturbations, improving the algorithm's accuracy and rate of convergence. Experimental results across various algorithms in different scenarios demonstrate that the DBOMF algorithm is capable of planning paths of a shorter and more uniform length, while exhibiting stronger obstacle avoidance capabilities and global optimum performance in complex environment.</p>