Named Entity Recognition is a key technology in natural language processing, and the methods based on deep learning have been widely used in Chinese entity recognition. Most deep learning models focus on the feature extraction of words and characters, but ignore the semantic information of word context, therefore, they cannot represent polysemy, and the performance of entity recognition needs to be further improved. In order to solve this problem, this study proposes a method based on the BERT-BiLSTM-CRF model. First, word vectors based on context information are generated by the pretreatment of BERT model, and then the trained word vector is input into BiLSTM-CRF model for further training. The experimental result shows that the proposed model achieves sound results and reaches F1-score of 94.65% and 95.67% respectively in the MSRA corpus and People’s Daily.