In order to alleviate the airspace congestion in Terminal Area and reduce the operational risk of aircraft, a flight trajectory prediction model based on back-propagation neural network (BP) was proposed. Firstly, the aircraft history data were filtered and denoised to obtain the reference trajectory; secondly, the trajectory similarity matrix based on Hausdorff distance was established, and all trajectories were classified automatically by using fuzzy C-means clustering (FCM); finally, considering the three-dimensional position, velocity and heading characteristics of flight trajectory, BP neural network was used to train and learn the trajectory characteristics. A flight trajectory prediction model was established to predict the multidimensional characteristics of the flight trajectory at the future time. The experimental results show that the prediction error of the network model is smaller, the prediction effect is better, and the flight trajectory prediction of aircraft can be more accurate.