This project applies Google BERT as a feature extraction layer before a BiLSTM+CRF architecture for Chinese named entity recognition. The BERT embeddings are fine-tuned during training on NER data from ChineseNER. It achieves approximately 93.68% F1 on the test set, improving over the non-BERT baseline by over two percentage points.