This paper aims to solve the high cost, low efficiency and local optimal path planning problems in the optimization of UAV delivery paths in low-altitude economic scenarios, and improve the global search ability and adaptability of logistics distribution path planning by improving the ant colony optimization algorithm. This paper constructs a mathematical model for distribution path optimization, taking into account multiple factors such as distance, energy consumption, and airspace safety, and improves the traditional ACO algorithm in combination with the low-altitude economic characteristics. The specific methods include introducing multi-dimensional heuristic information, dynamic pheromone update mechanism, elite ant strategy, and multi-population collaboration and migration strategy, which optimize the global search capability and convergence of the algorithm. The effectiveness of the algorithm was verified through simulation experiments. In the medical emergency material distribution scenario, the improved ACO completed the task in 25 min, which was 5 min less than the traditional ACO, 15 min less than the GA, and 10 min less than the PSO. The improved ACO algorithm shows higher efficiency and lower resource consumption than traditional methods in the optimization of logistics distribution paths under the background of low-altitude economy, and has strong application prospects. This study provides an effective path optimization solution for the logistics distribution system under the low-altitude economy, and provides theoretical support and practical guidance for the further development of drone delivery technology in the future.