To improve the ability of ResNet50 to extract target object features of remote sensing scene images and interpretability of scene classification, a Resnet50-CBAM-FCAM(RCF) network-based method of remote sensing image scene classification is proposed in this paper. This method increases the convolution attention module and full convolution-class activation mapping branch in the ResNet50 network. With the help of an attention mechanism, the branch features are fused with the extracted channel attention features and spatial attention features, respectively, and the class activation maps of various scenes are generated. The experimental results show that the overall classification accuracy of the proposed method in AID and NWPU-REISC45 datasets is more than 96% and 93%, respectively, and the visual results of the class activation maps can focus the target objects of remote sensing scene image accurately.