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In this paper, we present an end-to-end model based on modified Bidirectional Encoder Representations from Transformers (BERT) for Chinese named entity recognition (NER) in natural language processing. The model is composed of the SpanBERT layer and the Conditional Random Field (CRF) layer. By using combination, the model can express the input characters in the better form of "word embeddings", eliminating the steps of feature engineering or data processing in conventional approaches, and can be widely applied to the task of Chinese NER. Our experiments demonstrate that the SpanBERT-CRF model can effectively utilize the contextual data features and give more accurate recognition results. On our data set, the SpanBERT-CRF model had excellent performance with a recognition accuracy of 91.33%, outperforming the benchmark NER model BiLSTM-CRF (Bidirectional Long Short Term Memory, Conditional Random Field) and BERT-CRF model in performance and F1 score.