Quickly and accurately mining and analyzing the target association relationship contained in radar historical data can provide assistance for the study of air target activity law， target identification and judgment， and give full play to the role of historical data. According to the space-time characteristics of the target， a new method of mining and analyzing the association relationship of air targets is designed. A time matching method is designed to generate time correlation data sets. A spatial association metric is designed to generate spatial association data sets. Dynamic time warping （DTW） and spanning tree algorithm are used to match and search the air targets with associated relations， and generate the set of associated relations. After digital simulation verification， this method can effectively mine the historical information of radar data and find the correlation rules between targets.

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.