Multi-label scene classification (MLSC) of mining land (ML), that used to investigate whether there are some MLs, is of great significance for mine environmental monitoring and sustainable development. The ML’s characteristics of homogeneity and heterogeneity of spectral-spatial and topographic feature, large scale difference, and complexity of spatial co-occurrence greatly limit the accuracy of MLSC. This study has constructed a multi-modal dataset for MLSC of ML, by incorporating multispectral, synthetic aperture radar, and topographic data. And a novel fusion model that integrates attention-based cascaded convolution neural network (CNN) with label dependency-based graph convolution network (GCN) was proposed. The model consists of three main components. (1) Attention enhanced multi-scale feature cascade fusion, employed to extract crucial multi-scale features and reduce feature redundancy. (2) Label dependency-based GCN, utilizing multiple layers of GCN to extract spatial dependencies of ML from the label co-occurrence probability matrix. (3) Multi-modal feature fusion and multi-label classification, integrating the image features extracted by CNN with the spatial co-occurrence features extracted by GCN to obtain multi-label classification results. The mAP of the proposed model is 68.91%, outperforming other comparative models. Most of the other evaluation metrics also rank as either optimal or suboptimal for the proposed model. In summary, the dataset and model proposed in this study are beneficial for MLSC of ML.