In light of the issue of how to safely and efficiently navigate around restricted areas in the airspace during flight, a modified ant colony algorithm based on the A * algorithm was proposed for rerouting path planning. Firstly, through the construction of an airspace grid model, the flight airspace was divided into regular grid cells, and a discrete representation for path planning was provided. The shortcomings of the traditional ant colony algorithm in path planning, such as the blindness of search, slow convergence speed, and redundant turns, were focused on by the paper. Heuristic information in line with the A * algorithm and an initial pheromone distribution strategy were innovatively introduced. The suboptimal path generated by the A * algorithm was utilized to allocate initial pheromones, thereby accelerating the convergence of the algorithm. Additionally, a smoothing factor was designed to quantify the turning angle of the path, and the number of redundant turns was significantly reduced, enhancing the smoothness of the path. Furthermore, by systematically optimizing key parameters such as the heuristic factor, pheromone factor, pheromone evaporation factor, and smoothing factor, the robustness of the algorithm was further strengthened. The simulation results show that improved ant colony algorithm is found to outperform the traditional ant colony algorithm and the A * algorithm in terms of path length, number of turns, and average shortest path in the airspace grid model. In the verification of the actual airspace environment, the path planned by the improved ant colony algorithm is found to reduce fuel consumption by 3% ~ 4% compared to traditional methods, and the minimum turning radius is strictly compliant with the safety standards and performance constraints of the International Civil Aviation Organization. This proves that the improved ant colony algorithm enhances rerouting efficiency and reduces flight complexity while taking into account both safety and economy.