Aiming at the problems of early blind search, slow convergence and easy to fall into local optimum in the traditional ant colony algorithm for UAV 3D path planning, an improved ant colony algorithm is proposed in this paper. The algorithm uses spatial location to initialize the pheromone distribution and set a concentration threshold, which enhances the directionality of the early search of the ant colony and avoids the algorithm from falling into the local optimum. The heuristic function which takes into account both distance and direction factors is designed to improve the quality of path planning. The adaptive volatility factor is used to control the volatility of the pheromone, which improves the convergence speed of the algorithm. Compared with the traditional algorithm, two experiments show that the proposed algorithm reduces the average path length by 18.6%, the average iteration times by 63.3% and 78.7%, and the average corner times by 62.5% and 42.3%, respectively.