Abstract: In multi-label learning, a label may be determined by its own set of unique features only, which are called label-specific features. Using label-specific features in multi-label classification can effectively avoid some useless features affecting the performance of the constructed classification model. However, existing label-specific features methods only extract important features from the label’s perspective, while ignoring extracting important labels from the feature’s perspective. In fact, it’s easier to extract the unique features for labels by focusing on certain labels from the feature’s perspective. Based on this, a novel label-specific features learning algorithm for multi-label classification is proposed. It combines the label’s important features with the feature’s important labels. Firstly, in order to ensure the efficiency and accuracy of the model, the extreme learning machine is used to construct the joint learning model. Subsequently, the elastic network regularization theory is applied to the extreme learning machine’s loss function, and the mutual information theory is used to construct the correlation matrix of feature-specific labels as the L\-2 regularization term, and the label-specific features are extracted by the L\-1 regularization term. The learning model improves the deficiencies of label-specific features and the adaptability of the extreme learning machine in multi-label learning. Compared with several state-of-the-art algorithms on several benchmark multi-label datasets, the experimental results show the rationality and effectiveness of the proposed model.