Traditional multi-label learning algorithms generally do not consider the label imbalance， so the impact of label imbalance on classification is not ignored. However， statistics show that the current multi-label datasets have the problem of label imbalance， and a few kinds of labels are often more important. Based on this， this paper proposes an imbalanced multi-label learning algorithm based on classification interval enhanced （MLCIE）， which aims to enhance the learning efficiency and improve the quality of the sample label by using the reconstruction of each label classification interval， so as to reduce the impact of multi-label imbalance on the learning accuracy of the classifier. Firstly， the uncertainty coefficient of each label is calculated by using the density and conditional entropy of each label； Then the enhancement matrix of classification interval is constructed， so that the unique density information of each label is integrated into the original label matrix to obtain the balanced label space； Finally， the limit learning machine is used as the linear classifier for classification. In this paper， the proposed algorithm is compared with other seven multi-label learning algorithms on the 11 multi-label standard datasets. The results show that the proposed algorithm can solve the problem of label imbalance.