This study concentrates on the specific applications of artificial intelligence in civil aviation, with a particular focus on the autonomous scheduling of aircraft in the air. By using DBSCAN clustering algorithm to identify the terminal area trajectory pattern and combining it with the raster method to model the airspace in real time, the trajectory planning space is effectively reduced. Then, using the SARSA algorithm, autonomous aircraft heading adjustment in the approach control area is realized to effectively avoid conflicts. The research includes autonomous aircraft scheduling, terminal area definition, conflict resolution and trajectory prediction. By simulating the air traffic control environment, this paper not only verifies the advantages of the SARSA algorithm in improving the efficiency and safety of autonomous dispatch of aircraft, but also demonstrates the great potential of artificial intelligence technology in solving the problem of autonomous and safe dispatch of multiple aircraft types, especially in the Markov decision-making process.