Abstract: Abstract： In order to solve the problem of the decline of evaluation accuracy caused by the ignorance of situation elements with time by traditional threat assessment algorithms， this paper proposes a dynamic threat assessment method for air swarm targets based on intent recognition. In this method， the Long Short-Term Memory （LSTM） network is first used for intention prediction， and then the attention mechanism is used to improve the feature learning ability of the intention prediction model， and the multi-dimensional features of the input are weighted to a certain extent， so that the degree of influence of different features on the results is different. Softmax is used to classify the intention results， and then the results of intention prediction are used as important inputs for threat assessment in a cascading manner. Combined with static situation elements and dynamic situation elements at the current moment， multi-layer perceptron （MLP） is used for threat assessment. Simulation experiments show that compared with the traditional threat assessment method， the dynamic threat assessment method for air swarm targets based on intent recognition is more accurate.

Key words: Key words： intent prediction, threat assessment, neural networks, multilayer perceptron, attention mechanisms, long short-term memory networks