In order to solve the problem of missing aircraft target maneuver data sets, this paper uses kinematic modeling to generate a rich trajectory data set, which provides necessary data support for network training. In order to solve the problem that it is difficult to establish a kinematic model for trajectory prediction at the current stage and that it is difficult to extract spatiotemporal features with the time series prediction method, an aircraft target trajectory prediction method that combines the Transformer encoder and the Long Short Term Memory network (LSTM) is proposed. It can provide supplementary historical information and attention-based information representation provided by LSTM and Transformer modules at the same time, improving model capabilities. Through comparative analysis with some classic neural network models on the data set, it is found that the average displacement error of this method is reduced to 0.22, which is significantly better than 0.35 of the CNN-LSTM-Attention model. Compared with other networks, this algorithm can extract hidden features in complex trajectories. When facing complex aircraft trajectories with continuous turns and large maneuvers, it can ensure the robustness of the model and improve the accuracy of prediction of complex trajectories.