Due to the status of the Unmanned Combat Aerial Vehicle (UCAV) itself and the situation of the air combat, there are many uncertainties in UCAV aerial combat, including the maneuvering and choosing of behavioral strategy of the two sides. To solve this problem, the reinforcement learning method is introduced into UCAV air combat, and the UCAV maneuvering model and action set are established.The function of air combat situation assessment is taken as the signal function for reinforcement learning, and the Probabilistic Neural Network (PNN) is used as the unit for enemy maneuver prediction. Under the condition of thorough perception of battlefield information of both sides, the UCAV can grasp the best maneuvering strategy through interacting with the environment by reinforcement learning, thus to implement one-on-one aerial combat.