The objects of the real world can be assigned multiple meaning, with a variety of non-single labels. As to multi-label learning, although the related current work may take advantage of the reuse score to analyze the relationship between multiple labels, it still can find neither the label structure nor the main labels and importance rankings. The nonnegative matrix factorization (NMF) method can divide associated nodes into societies effectively, and explore the potential relationship between them. Consequently, it is worth studying how to use NMF in multi-label community detection. Here, an algorithm is proposed for multi-label community detection, which can analysis labels effectively and discover the community structure inside, and then obtain relations community. Besides, these multi-label nodes can be sorted according to their importance scores, and then the master-slave structure of these marked nodes can be obtained and the effectiveness of this algorithm is thus verified, which helps us learn the hidden information