A shallow Convolution Neural Network (CNN) is designed to replace the fully-connected layer in the pre-training model. The CNN feature extracted by the pre-training network is taken as input image to the designed shallow CNN network. Compared with the fine-tuning method for the pre-training model, this method can better adapt to the aerial image localization tasks. In order to further improve the locating accuracy of aerial images, the Bi-LSTM network is added to the network at the CNN classification stage by use of the temporally-continuous characteristics of the UAV aerial image. Thus the features of multiple images can be taken as the criterion for the network classification. Experiments show that the accuracy of sequential image locating method reaches a stable level at around 0. 89, and is improved by about 5% compared with the single-image locating method.