LDA (Latent Dirichlet Allocation) is widely used in text analysis and images processing.However

LDA and most of its modifications are unsupervised learning models

which are not appropriate for classification especially multi-label classification problem.Through the study on the multi-label documents and LDA models

this paper proposes a new Labeled LDA model

namely Shared Background Topics Labeled LDA(SBTL-LDA).In this new model

each label has not only a set of local topics

but also has several background (global) topics.Experienmental results show that SBTL-LDA can decrease the affect of similarities and dependence between different topics and because the label of document is mapped as a combination of local topics and shared topics

so it has a high accuracy when learning from multi-Labeled documents.In addition

this model can be viewed as a semi-supervised clustering model which can utilize the information of labels and outperfom other models.