To solve the problem of stealth UAV penetration path planning in complex battlefield environment, an Improved Ant Colony Optimization (IACO) is proposed.The weight of interaction between adjacent nodes and the path selection weight are introduced, and the improved heuristic function is adopted to enhance the guidance of the target point area, so as to improve the search efficiency and maintain the diversity of solutions.The mode of pheromone dynamic adjustment is designed, and a new pheromone concentration updating strategy is proposed to enhance the global search ability.The simulation results show that, compared with the traditional Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO), the IACO can effectively avoid the threat of networked radar and thus improve the survivability of stealth UAVs.In addition, the proposed method has better performance in computing efficiency and safety, which verifies the effectiveness and superiority of the IACO.