With the advantages of high transmission rate and great security,the Aeronautical Mobile Airport Communications System (AeroMACS) has become an important part of the airport and air-ground communication network.Aiming at the problems of outdated Channel State Information (CSI) caused by fast time-varying channel during the high-speed movement phase of aircraft take-off and landing,and worsening of communication quality caused by large Doppler frequency shifts,a channel prediction method based on transformer neural-network is proposed by using a multi-head self-attention mechanism.Modulation Coding Scheme (MCS) for WiMAX and 5G dual-mode of AeroMACS is adjusted according to the Signal-to-Noise Ratio (SNR) predicted in real time.Simulation results show that,compared with the other three artificial intelligence methods,the proposed transformer network-based channel prediction method achieves higher accuracy and enhances the total throughput of the system,which can effectively cope with the problem of outdated CSI and improve the system communication performance.