Abstract: To solve the problem that multi-label learning with label specific features（LIFT） could not consider label correlation in the clustering and classification stages, a method for multi-label learning with label-specific features via label correlations（LFLC） was proposed. The label space was added to the feature space for clustering to construct the classification model, and the clustering ensemble with considering label correlation was used to construct label-specific features for each label. The correlation matrix was used to construct undirected complete graph and mine the correlation of label sets in the graph. The strong correlation of multiple different structures between labels was expressed by tree ensemble. In the experiment, 10 data sets covering different fields were used, and Hamming Loss, Ranking Loss, One-error, Coverage, Average Precision and macroAUC were used as evaluation indexes to carry out parameter sensitivity analysis and statistical hypothesis test. The results show that the LFLC algorithm combined with clustering ensemble and strong correlation between labels can obtain better performance generally.