UAV in complex battlefield environment, because the two sides of the UAV shape, color and other characteristics are more similar, how to identity enemy UAV accurately is the key to realize the autonomous navigation and combat mission execution. However, due to changes in the speed, shape, size, attitude of enemy UAV and the impact of meteorological and environmental factors, they can not be accurately identified and classified. Aiming at this problem, a kind of sparse auto-encoder(SAE) based on the transfer learning was proposed, and the target objects in the multi-frame aerial images were identified. The algorithm firstly used SAE to study the unsupervised learning of a large number of unmarked samples in the data concentration of source domain, and obtained its local characteristics. Then, the global feature response of the aerial image in the target domain was extracted by the convolution neural network (CNN) algorithm. Finally, the different categories of target objects were identified and classified by the Softmax regression model. The experimental results show that new algorithm proposed in this paper for multiple target objects in aerial multi-frame images is better than more traditional non-transfer learning SAE algorithm, and underlying visual feature recognition transfer learning algorithm, which has higher recognition rate.