Due to the complexity of the background in aerial images and the diversity of object categories, aerial image classification is a challenging task. In order to address the problems of low accuracy and poor generalization in traditional multi-label aerial image classification methods, a method based on recurrent neural networks was proposed.In this method, the super-pixel segmentation algorithm was first used to obtain the low-level features of the image from which an attention map was generated. Subsequently, the best image scale was obtained by cross-validation,and multi-scale attention feature graphs were embedded into aconvolutional neural network in order to extract the features of the image.Finally, tomine the correlation between labels,an improved bidirectional Long Short-Term Memory (LSTM)network was proposed, which increases the connection from the input gate to the output gate, so that the input state can efficiently control the output information of each memory unit. The forget gate and the input gate were combined into a single update gate so that the improved bidirectional LSTM network can learn long-term historical information. The results obtained by applying the proposed method to the UCM multi-label dataset indicate that for scale values of 1,1.3, and 2, the accuracy and recall rates of the model are 85.33% and 87.05% respectively,while the F1 score reached 0.862. The accuracyand recall rates are found to be higher than those of theVGGNet16 model by 7.25% and 8.94% respectively.The experimental results thus indicate that the proposed method can effectively increase the accuracy of multi-label aerial image classification.