Current remote-sensing scene classification methods do not fully utilize multi-scale and contextual information, which limits scene classification performance. To address these issues, a multi-scale context feature aggregation model based on a graph convolutional network (GCN) is proposed. In the image feature extraction module, multi-layer and global features of remote sensing images are extracted using the backbone network. Next, in the contextual information enhancement module, contextual information is extracted from multi-layer features utilizing the GCN. Then, in the multi-scale feature aggregation module, a progressive cross-layer attention method is used to model the correlation between different layer features with the aim of reducing semantic differences and achieving effective feature aggregation. Finally, global and aggregated features are fused to achieve scene classification, and label smoothing loss is used to enhance model generalization. Experimental results on the AID and NWPU-RESISC45 datasets validate the effectiveness of the proposed model, which achieves competitive performance in scene classification.