Although the exploitation of mineral areas brings wealth to society, it inevitably leads to the degradation of the surrounding natural environment. To understand and assess the influences of mining activities on the geological and ecological environment, land cover classification in open-pit mine areas (LCCMA) is of great significance. This research proposes an intelligent classification framework for LCCMA based on an object-oriented method and multitask learning (MTL), named the MTL Classification Framework (MTLCF). With the help of MTL, each land cover type in open-pit mine areas obtains its exclusive and receivable object-oriented feature sets using the model-agnostic method. After that, the feature sets are fused with the original images. EfficientNet, a spatial pyramid pooling module, and a global attention upsample module are assembled as the segmentation models with the structure of the encoder and decoder to classify intelligently each land cover type in open-pit mine areas. Finally, the models were trained, and ablation experiments were performed. The experimental results show that our proposed framework -MTLCF was effective for classification in LCCMA, and the overall accuracy and the mean of F1 score for the MTLCF in LCCMA were 85.6% and 86.06%, respectively.