To overcome the shortcomings of traditional methods in solving three-dimensional UAV path planning,such as high planning costs,poor accuracy and prone to obtain a local optimum,a three-dimensional UAV path planning algorithm based on Q-learning arithmetic optimization algorithm is proposed.In order to improve the optimization accuracy of the arithmetic optimization algorithm,the Circle chaotic mapping is introduced to improve the diversity and distribution uniformity of the initial population.Q-learning is introduced to adaptively adjust the updating of the acceleration function.The global searching and local development of the algorithm are made balanced.The perturbations in the optimal solution’s neighborhood are designed to optimize the global searching capability.By establishing a three-dimensional UAV path planning model,the path planning is transformed into a multi-objective function optimization problem,and the improved algorithm is used to solve the three-dimensional UAV path planning problem.The trajectory objective function that comprehensively considers the trajectory cost,the terrain cost and the boundary cost is used to evaluate the fitness of the particles,and the path planning is optimized through iterations.The simulation results show that the trajectory obtained by the proposed algorithm has lower total costs and the stability to adapt to different complex terrain environments.