To address the issues of low efficiency and insufficient feasibility in path planning for drones in complex three-dimensional terrain, focusing on path planning in natural terrain scenarios, with urban environments and dynamic obstacle scenarios as future research directions, an adaptive hybrid optimisation method combining the A* algorithm and jellyfish search algorithm was proposed. The method introduces an adaptive parameter adjustment mechanism, which effectively improves the efficiency of path planning and the feasibility of the results. Experiments were carried out using shuttle radar topography mission(SRTM) measured terrain data and typical simulation environments to compare the commonly used optimisation algorithms such as A* algorithm, particle swarm optimisation algorithm, ant colony optimisation algorithm and whale optimisation algorithm. The experimental results show that the method achieves significant advantages in a number of indicators, such as path length, computation time and memory consumption. Specifically, it is shown that the overall path is more optimised, the planning speed is increased, the resource consumption is reduced, and the path feasibility and safety can be ensured under the complex terrain environment. In addition, the multi-constraint dynamic cooperative strategy and the efficient hybrid planner architecture further enhance the adaptability of the algorithm to different terrains and complex constraints. The results provide a new engineering implementation idea for autonomous path planning of UAVs in complex environments, which has important application value and promotion prospects.