Ion-adsorption-type rare earth minerals are important strategic resources. Long-term extensive mining has led to severe surface damage in mining areas, posing significant challenges to the ecological environment. Accurate and detailed land use information is a critical foundation for ecological restoration and process monitoring in mining areas. Hyperspectral imagery is considered an effective means for large-scale monitoring of mining areas to obtain land use information. However, the complexity of the land cover and the information redundancy in hyperspectral images pose challenges for fine classification. This study proposes a fine classification method for rare earth mining areas based on object-oriented thinking and a multi-layer attention convolutional neural network (OCTC). First, a scale parameter estimation model was used to quantitatively analyze the optimal segmentation scale at multiple levels of the rare earth mining area images. Four types of image features—spectral, index, texture, and geometric—were extracted from the segmented images. Then, an optimal feature combination was obtained through distance separability analysis. Based on this, a multi-layer attention convolutional neural network model (OCTC) was used for classification. This model is an improved version of the 1D-CNN, integrating the Transformer and CBAM to enhance the model's feature extraction capabilities and overall classification accuracy. To verify the method's effectiveness, Zhuhai-1 hyperspectral remote sensing imagery was used as the data source, and the Jiangxi Gan'nan Lingbei rare earth mining area served as the study region. The proposed method was compared with KNN, RF, and 1D-CNN classification methods for accuracy analysis. The results demonstrate that the proposed method effectively mitigates salt-and-pepper noise, maintains good overall classification integrity, and achieves the highest classification accuracy. The overall accuracy reached 88.11%, representing an improvement of 1.22% to 8.84% compared to other classification methods, with the Kappa coefficient increasing by 0.015 9 to 0.109 0. This method can provide valuable reference and scientific insights for the fine classification of land use and production monitoring, as well as environmental protection management in rare earth mining areas.