Abstract: Aiming at solving the characterization and optimization of information freshness in the sixth generation (6G) communication system, we firstly model information freshness based on the age of information (AoI) in the unmanned aerial vehicle (UAV) communication system and formulate an AoI minimization problem subjected to the energy consumption. However, the nonconvex problem is difficult to solve due to discreteness of AoI optimization and the complicated energy consumption expression. A reinforcement learning-based scheme is proposed to design the UAV’s trajectory, in which the reward function related to AoI is constructed to realize a fast and intelligent UAV trajectory decision, thus reducing the AoI of UAV communication system. The simulation results show that, compared with the benchmark schemes, the proposed trajectory design scheme can improve the information freshness by 8.51%～21.82%. In addition, the proposed scheme has a superior convergence.