With the rapid development of artificial intelligence and the proposal of “Smart Airport”, it is of great importance to actively explore the application of AI in airports to assist airport controllers and pilots to command aircrafts to taxiing on the aircraft ground effectively. Firstly, a reinforcement learning mobile model of aircraft airport is constructed, and then Meilan Airport of Haikou is taken as an example to achieve the scene simulation by using the Python built-in toolkit Tkinter. Considering the aircraft taxiing rules of the airport, the Q -Learning algorithm in Off-policy is used to solve the Bellman equation, which achieves the requirement of AI static path planning of aircraft in the model-based environment. Finally, the simulation results show the effectiveness of the proposed method.。