Multi-label feature selection is an important research component in the field of multi-label learning. Existing multi-label feature selection methods mainly measure the importance of each feature based on the dependency between features and labels， and the redundancy among features. Then， feature ranking is performed based on feature importance， often ignoring the influence of label relationships on feature importance. To solve this problem， a multi-label feature selection algorithm based on label complementarity（MLLC） is designed， which introduces neighbourhood mutual information. The algorithm takes dependency， redundancy and label relationships as the evaluation elements of feature importance. And then it redesigns the feature importance evaluation function based on these three elements， so as to select features with stronger discriminative power and achieve better classification performance. Finally， the effectiveness and robustness of the algorithm are verified on six classical multi-label datasets.