The narrow and complex deck space necessitates that the carrier-based aircrafts follow planned paths during taxiing operations, making the dispatch paths critical to both safety and efficiency. Based on the advantages of the reinforcement learning method in solution efficiency, the PPO reinforcement learning method is adopted to establish carrier-based aircraft dispatch path planning method. A termination region is designed and the end-of-episode reward is improved, so that the path can satisfy the start and end point direction constraints at the same time. The obstacle detectors are introduced into the path planning agent and the random environment training are used to improve the adaptability of the agent in different environments. The rationality of the model and the effectiveness of the method are verified by simulation finally.