Feature selection is the key pre-processing step of multi-label learning tasks. It can efficiently solve the problem of the “curse of dimensionality”， which is existed in the high-dimensional multi-label data. In multi-label learning， the label is described as the form of logical distribution， in which the importance of each label associated with the instance is equivalent. However， the label importance of each label is usually different in many fields. For this issue， a label enhancement algorithm is proposed in this paper. By evaluating the fuzzy similarity relation on labels among instances， it transforms the multi-label data to the label distribution data. The discernibility relation on labels and the fuzzy relative discernibility relation on features are analyzed in details for label distribution data， then the fuzzy discernibility on the label space and the feature space is defined， and the significance of feature is constructed to assess the discernibility ability of the feature. On this basis， a feature selection algorithm is proposed for label distribution data， which can obtain the result of feature selection in descending order of feature significance. Finally， the experimental results show that the proposed algorithm is effective and feasible on several multi-label datasets.