The BLSTM-CRF neural network model is built to improve the performance of Chinese domain named entity recognition in the absence of feature engineering.First，the CBOW model was used to carry out recursion training of negative sampling on the corpus of People's Daily from January to June in 1998 to generate a low-dimensional dense word vector table for the query needs；then，based on Boson entity corpus，the word vector was formed by querying the word vector table，and the information feature vector of the words in the corpus was obtained by using the Jieba participle；finally，the combined word vector and word information feature vector are input into BLSTM-CRF deep neural network.Experimental results show that the model can be well identified for the Chinese domain named entities，and the F1 value is up to 91.86%.