Aiming at the problems of traditional trajectory prediction methods, such as large model simplification and less consideration, combined with the characteristics of flight trajectory continuity, time series and interactivity, a trajectory prediction method based on bidirectional long short term memory neural network is proposed. The position, heading, pitch, roll and relative information of the intruder UAV are simultaneously used as the input of the trajectory prediction model, which is more in line with the true trajectory change law. A Bi-LSTM-based trajectory prediction model is established, which can simultaneously learn the implicit information in the forward and backward trajectories, and adopt the adaptive learning rate learning algorithm to train the model. The simulation results show that compared with the Elman neural network, the average absolute error of the model predicted by 200 points in different directions is less than 4m, and the 3D prediction effect is better, and the trajectory prediction can be performed more accurately.