Aiming at the problem that the pre-training model BERT （Bidirectional Encoder Representation from Transformers） lacks of vocabulary information， a Chinese named entity recognition model called OpenKG + Entity Enhanced BERT + CRF （Conditional Random Field） based on knowledge base entity enhanced BERT model was proposed on the basis of the semi-supervised entity enhanced minimum mean-square error pre-training model. Firstly， documents were downloaded from Chinese general encyclopedia knowledge base CN-DBPedia and entities were extracted by Jieba Chinese text segmentation to expand entity dictionary. Then， the entities in the dictionary were embedded into BERT for pre-training. And the word vectors obtained from the training were input into Bidirectional Long-Short-Term Memory network （BiLSTM） for feature extraction. Finally， the results were corrected by CRF and output. Model validation was performed on datasets CLUENER 2020 and MSRA， and the proposed model was compared with Entity Enhanced BERT pre-training， BERT+BiLSTM， ERNIE and BiLSTM+CRF models. Experimental results show that compared with these four models， the proposed model has the F1 score increased by 1.63 percentage points and 1.1 percentage points， 3.93 percentage points and 5.35 percentage points， 2.42 percentage points and 4.63 percentage points， 6.79 and 7.55 percentage points， respectively in the two datasets. It can be seen that the comprehensive effect of the proposed model on named entity recognition is effectively improved， and the F1 scores of the model are better than those of the comparison models.