Due to the small number of remote sensing image datasets, it is difficult to train deep neural networks, so we first constructed a two-branch network based on exponentially learning multi-labeled remote sensing image features. In addition, most multi-labeled remote sensing image classification networks use ResNet as the backbone network, which ignores inter-channel correlation, so we used SE-ResNet as the two-branch backbone network. Finally, since most traditional methods focus only on the visual elements in an image or only on the dependencies between multi-labels, we combined the two and constructed a multi-label remote sensing image classification network, Dual-branching Channel Attention and Graph Convolution Network (DCA-GCN), based on a two-branch network and graph convolution, using the two-branch channel attention structure to extract richer image features from remote sensing images and the graph convolution network to establish the dependencies between multi-labels. DCA-GCN achieves relatively excellent results on three publicly available multi-label remote sensing datasets.