The Unmanned aerial vehicles three-dimensional path planning problem is a combinatorial optimization problem to find the optimal path between the starting point and the endpoint in complex three-dimensional environment, but most path planning algorithms struggle to find feasible paths within acceptable time and precision range, therefore, a dynamic multi-subswarm salp swarm algorithm based on K-means++ clustering optimization was proposed to address the aforementioned issue. Firstly, a new cost function incorporating height cost was proposed within the three-dimensional environment model. The path planning problem was converted into a multi-dimensional function optimization issue. Secondly, the population was clustered using the K-means++ clustering algorithm, and a dynamic multi-subswarm mechanism was designed to balance the algorithm's global search and local exploitation. Each subswarm collaborates with multiple strategies for improvement, avoiding the algorithm from being trapped in local optima while enhancing global optimization capability. Finally, after validating the algorithm against five algorithms ISSA, MSNSSA, IBSO, MBFPA, and SSA using 12 CEC2017 benchmark test functions, it was applied to solve the optimal path planning problem in three-dimensional environments. Simulation results under different environmental models demonstrate that the algorithm's average effective path rate is increased by 15.5%, 11%, 23%, 20.5% and 18% compared to the other five algorithms, confirming its excellent optimization capability in complex environments.