Previous studies on the UAV swarm air combat decision-making problem lack consideration of uncertainty, intense confrontation, and high dynamics of aerial combat scenarios. This paper delves into the target allocation decision problem of the UAV attack and defense confrontation in a dynamic aerial combat environment. First, a dynamic game model is proposed to adapt changes in combat resources and adversaries’ behavior throughout multi-stage game processes. Then, the corresponding Nash equilibrium is solved based on a game learning algorithm- the Q-Learning algorithm. Finally, we consider the case where the UAV destructive probability is unknown, an uncertainty model is designed for predicting the UAV destructive probability based on the fuzzy comprehensive evaluation method.