The air battlefield situation provides a general description of the actions and states of all participants， while target combat intention recognition serves as a critical foundation for air battlefield posture assessment. To address the challenge of acquiring a large amount of labeled air target battlefield data amidst intense confrontation and rapidly evolving scenarios， a semi-supervised air targets combat intention recognition model is proposed based on metric learning. The model offers a method for uncovering potential patterns from unlabeled samples， thus reducing the reliance on extensive labeled data. In this model， the target time-series data encoder reduces the dimensionality of target data and produces an embedded representation. Based on this， loss values are calculated by measuring the similarity between labeled target sequences and intention types， as well as between labeled and unlabeled target sequences. The experimental results demonstrate that the model achieves combat intention recognition accuracy rates of 86%， 89%， and 91% with labeled sample rates of 30%， 40%， and 50%， respectively.

Threats often have the characteristics of dynamic evolution with the development of bilateral confrontation. Traditional threat assessment methods are more based on static threats and lack of prediction of dynamic threats. To solve this problem， this paper proposes a threat assessment method for aerial targets under confrontational conditions. Setting the red side as the attacking side and the blueside as the defending side. The reinforcement learning model is established with the enemy target as the agent， and its state space， action space， transition function and reward function are designed. The threat assessment model is established， the threat element index is established， and the threat assessment method is designed. The model is trained， and the trained model can predict the enemy aerial target threat according to the confrontation situation. Through test and analysis， the method is more reasonable for threat assessment of enemy aerial targets under confrontation conditions.