Autonomous path planning of UAV is a key technical problem for future UAV operation. In view of the shortcomings of traditional route planning methods, such as low efficiency, poor real-time performance, easy to fall into local optimum, this paper proposes an improved heuristic ant colony algorithm for UAV route planning. In the early stage of the algorithm, Dijkstra algorithm is used to initialize the track, and heuristic information is introduced to improve the search efficiency; Logistic chaotic map is used to initialize pheromone to increase the diversity of solutions and improve the convergence speed of the algorithm; in the middle and late stage of the algorithm, multi track selection strategy and simulated annealing mechanism are used to improve the global search ability of the algorithm and avoid falling into local optimum due to too fast convergence speed Solution. The simulation results show that in the complex environment with threats and obstacles, compared with the basic ant colony algorithm, the improved ant colony algorithm can effectively plan a path from the start to the end, and has higher optimization accuracy and faster convergence speed, which has a certain application value.