To solve the problems of missing target features and strong clutter interference in infrared weak target detection and tracking in complex backgrounds, the target motion feature is introduced into the multi-frame processing method to improve the performance. However, for highly maneuverable targets such as UAVs, the existing multi-frame processing methods based on mechanism models make it difficult to cover their complex and variable motion forms, which makes the result of detection and tracking unsatisfactory. In this regard, a data-driven infrared weak maneuvering target detection and tracking method under complex backgrounds based on a multi-frame processing framework is proposed. Firstly, the MPCM algorithm is used to enhance weak targets. Then, the enhanced results of multiple frames are projected onto a 2D subspace to construct a 2D trajectory detection model based on YOLO. Finally, the 2D detection results are backtracked in 3D space-time to construct a 3D trajectory detection model based on LSTM. When constructing the detection model, data augmentation is performed on the images of real image to ensure that the training samples cover as many target motion forms as possible. In the 2D subspace, a high, performance YOLO detection network is used to quickly eliminate a large amount of clutter. In the 3D space-time, a small amount of difficult-to-eliminate clutter is finely screened out by the LSTM temporal network. The results of comparison experiment demonstrate that the proposed method is capable of achieving real-time performance in terms of time consumption, and the method exhibits an average tracking accuracy of 86.6% across multiple scenarios, a remarkably low false alarm rate of only 9.2%, and an impressive AUC value of 0.982 0 for the ROC curve.