Label modeling is the basic task of label system construction and portrait construction. Traditional label modeling methods have problems such as difficulty in processing fuzzy labels， unreasonable label extraction， and ineffective integration of multi-modal entities and multi-dimensional relationships. Aiming at these problems， an enterprise profile construction method based on label layering and deepening modeling， called EPLLD （Enterprise Portrait of Label Layering and Deepening）， was proposed. Firstly， the multi-characteristic information was extracted through multi-source information fusion， and the fuzzy labels of enterprises （such as labels in wholesale and retail industries that cannot fully summarize the characteristics of enterprises） were counted and screened. Secondly， the professional domain lexicon was established for feature expansion， and the BERT （Bidirectional Encoder Representation from Transformers） language model was combined for multi-feature extraction. Thirdly， Bi-directional Long Short-Term Memory （BiLSTM） was used to obtain fuzzy label deepening results. Finally， the keywords were extracted through TF-IDF （Term Frequency-Inverse Document Frequency）， TextRank， and Latent Dirichlet Allocation （LDA） model to achieve label layering and deepening modeling. Experimental analysis on the same enterprise dataset shows that the precision of EPLLD in the fuzzy label deepening task is 91.11%， which is higher than those of 8 label processing methods such as BiLSTM+Attention and BERT+Deep CNN.