With the rapid development of current air combat weaponry, trajectory prediction for high-altitude, high-speed, large maneuver targets is occupying an increasingly important strategic position. In order to solve the current problem of insufficient target trajectory prediction, this paper proposes a model integrating wavelet decomposition (WD) and long short term memory (LSTM) network to predict the trajectory of maneuvering targets. First, the input trajectory time series is decomposed into one low frequency component (CD1) and three high frequency components ( CA1, CA2, CA3) by wavelet decomposition. Then, the component prediction is performed by taking advantage of the long short term memory network for time series processing. Finally, the component prediction results are reconstructed and compared with the original trajectories for verification, and the results show that the proposed model has high accuracy for trajectory prediction. In order to exclude the chance of experimental results, two sets of data are used for validation in this paper. The comparison experiments show that the proposed model has less prediction error compared with the other two models.