Unmanned aerial vehicle （UAV） cooperative combat is crucial in modern warfare. The cooperative mode between unmanned combat aerial vehicles （UCAVs） and decoy UAVs has gained significant attention due to its tactical value. This paper proposes a cooperative trajectory planning method based on the proximal policy optimization （PPO） algorithm for UCAV and decoy UAV strike missions against key enemy targets. We construct a Markov decision process （MDP） model incorporating dynamic threat assessment， integrating UAV kinematics and battlefield constraints， and design the state/action spaces and a hierarchical reward function. Simulation results demonstrate that the proposed method effectively guides UCAVs and decoys to achieve efficient cooperation in complex environments， significantly increasing mission success rates while reducing interception risks from enemy air defense systems. This provides theoretical and technical support for intelligent path planning in UAV cooperative operations.