In order to solve the problems of small number of training samples and complex background of remote sensing images, this paper introduces transfer learning and channel attention into convolutional neural network (CNN), and proposes a remote sensing image scene classification method based on transfer learning and channel attention. Firstly, this method selects two CNNs pre-trained by ImageNet natural dataset as the backbone, and introduces the channel attention mechanism to adaptively enhance the main features and suppress the secondary features. Then the features extracted from these two networks are fused for classification. Finally, fine-tuning transfer learning is used to realize learning and classification in the target domain. The proposed method is evaluated on several classical public datasets, and the experimental results show that the proposed method achieves the same performance as other advanced methods in remote sensing image scene classification.