Provided is a tax document hierarchical classification method based on multi-tag classification. Firstly, generated subject distribution is extracted from a latent Dirichlet allocation model, and a latent Dirichlet allocation topic character of a tax file is built; then, tf idf feature vectors corresponding to training data are built, the tf idf feature vectors including the training data and files to be classified are calculated, and similarity is calculated to obtain candidate category tags; finally, source data of candidate category tag nodes are supplemented with auxiliary data, a multi-tag classification model based on transfer learning is built through a transfer learning algorithm TrAdaBoost, and the files to be classified are classified. According to the method, a hierarchical classification problem is converted into a searching stage and a classification stage, calculated amount is greatly reduced by means of incremental candidate category searching, computation complexity is lowered, the tax files are mapped to tax category hierarchical categories by means of the multi-tag classification model based on transfer learning, the auxiliary data are effectively used, and classification performance is improved.