Aiming at the problem that most of the current Named Entity Recognition （NER） models only use character-level information encoding and lack text hierarchical information extraction， a Chinese NER （CNER） model incorporating Multi-granularity linguistic knowledge and Hierarchical information （CMH） was proposed. First， the text was encoded using a model that had been pre-trained with multi-granularity linguistic knowledge， so that the model could capture both fine-grained and coarse-grained linguistic information of the text， and thus better characterize the corpus. Second， hierarchical information was extracted using the ON-LSTM （Ordered Neurons Long Short-Term Memory network） model， in order to utilize the hierarchical structural information of the text itself and enhance the temporal relationships between codes. Finally， at the decoding end of the model， incorporated with the word segmentation Information of the text， the entity recognition problem was transformed into a table filling problem in order to better solve the entity overlapping problem and obtain more accurate entity recognition results. Meanwhile， in order to solve the problem of poor migration ability of the current models in different domains， the concept of universal entity recognition was proposed， and a set of universal NER dataset MDNER （Multi-Domain NER dataset） was constructed to enhance the generalization ability of the model in multiple domains by filtering the universal entity types in multiple domains. To validate the effectiveness of the proposed model， experiments were conducted on the datasets Resume， Weibo， and MSRA， and the F1 values were improved by 0.94， 4.95 and 1.58 percentage points， respectively， compared to the MECT （Multi-metadata Embedding based Cross-Transformer） model. In order to verify the proposed model’s entity recognition effect in multi-domain， experiments were conducted on MDNER， and the F1 value reached 95.29%. The experimental results show that the pre-training of multi-granularity linguistic knowledge， the extraction of hierarchical structural information of the text， and the efficient pointer decoder are crucial for the performance promotion of the model.