Aiming at the decision-making problem of beyond visual range （BVR） air combat， a decision rule tree model that integrates prior knowledge of tactical implementation distance with evolutionary iteration capabilities is established. The mechanism of air combat victory is analyzed， the BVR air combat situation model and the tactical implementation distance model are established， the BVR air combat factors are introduced and the posture assessment model and the set of decision variables are established； the BVR air combat tactical action decomposition and the construction of the action library are implemented； the BVR air combat Decision tree are established， a new particle swarm optimization method based on dynamic adjustment of inertia weights and chaotic optimization is proposed to improve the evolution efficiency realization of the decision rule tree； based on the simulation experiment， the decision effect analysis of the evolutionary decision tree under different conditions is given. The simulation results show that the evolutionary decision tree proposed can effectively integrate a priori knowledge of tactical implementation distance， missile strike zone， etc.， and has the evolutionary capability to effectively solve the BVR tactical decision problem.