Reinforcement learning （RL） performance in beyond-visual-range （BVR） air combat is constrained by inadequate training opponents. This paper proposes a rule-based agent decision framework serving as RL training adversaries， where simulations confirm significantly enhanced combat effectiveness through efficient mastery of tactical maneuvers and improved adaptive decision-making. Fundamental aircraft maneuvers are modeled within an air combat simulation environment with collaborative strategy training modules. To address incomplete coverage and complexity in conventional rule-based decision trees， a state-machine-driven framework implements event-condition mechanisms for state transitions and combat decisions， demonstrating superior performance in comparative simulations. Finally， RL agents trained against this state-machine-based opponent under expert knowledge guidance autonomously acquire classical maneuvers while exhibiting advanced decision adaptability， providing foundational insights for BVR decision systems.