With the development of UAV technology, the route planning, obstacle avoidance and optimal flight path selection of UAV in the course of flight have become crucial. Ant colony algorithm, as an intelligent optimization algorithm, has a very powerful search ability and has been widely used in the field of route optimization. However, ant colony algorithm has some problems, such as slow convergence speed, local optimization and contradiction between population diversity and convergence speed. In this paper, the global improved ant colony algorithm is studied from the aspects of space modeling, obstacle shielding, guided path, adaptive random step algorithm, pheromone update and optimized route. From the three aspects of theoretical introduction, simulation experiment and experimental comparison, it is concluded that the improved ant colony algorithm has been significantly improved in terms of search performance and search efficiency.