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With the further deepening of artificial intelligence research and the shine of related technologies on the battlefield of Russia and Ukraine, its role in the military field has become more and more important. In view of the increasingly complex battlefield environment, the current missile penetration field has problems such as high information dimension, slow command response, and inflexible penetration maneuver tactics. In this paper, a training method based on multi-agent deep deterministic strategy gradient (MADDPG) is proposed to quickly generate missile attack maneuver schemes to assist commanders in making battlefield decisions. At the same time, the experience playback strategy of the algorithm is improved, and the experience pool filtering mechanism is added to shorten the training time and meet the rapid response requirements in real scenarios. By setting the multi-target rapid interception strategy, the simulation verifies the maneuvering strategy advantages of the designed method that can penetrate defense, intelligently and collaboratively strikes the target, and verifies that the method can improve the convergence speed and 10% success rate by 8% compared with other algorithms through comparison.