The current research focuses on the physical security of UAV, while there are few studies on UAV information security. Moreover, the frequency of various security problems caused by UAV has been increasing in recent years, so research on UAV information security is urgent. In order to solve the high cost of UAV experiments, complex protocol types, and hidden security problems, we designe a UAV cyber range and analyze the attack and defense scenarios of three types of honeypot deployment. On this basis, we propose a UAV honeypot active defense strategy based on reinforcement learning. The active defense model of UAV honeypot is described of four dimensions: state, action, reward, and strategy. The simulation results show that the UAV honeypot strategy can maximize the capture of attacker data, which has important theoretical significance for the research of UAV information security.