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Recognizing named entities from raw text is the first step to construct a fresh egg supply chain knowledge graph and support a variety of downstream natural language processing tasks. This task can sort out the information in the supply chain and provide a basis for food safety traceability. In the raw text of fresh egg supply chain, there were various types of entities, and feature information extraction was inefficient. In order to solve the problem of fast and accurate identification of the named entities which entity types were pre-defined, a bidirectional encoder representations from transformers-conditional random field (BERT-CRF) architecture was proposed to solve the task of named entity recognition (NER) in the area of fresh egg supply chain. In BERT-CRF architecture, begin, internal and other (BIO) labeling rule was used to label the sequence, and the concatenation of character vector and position vector was used as inputs. The pre-training language model (BERT) was used to obtain the global features of input sequence, and the CRF layer was added at the end of the model to introduce hard constraints. A comparative experiment was conducted with other three NER model on the self-constructed dataset that contained five categories and 21 subcategories. The result showed that the BERT-CRF model was superior to the others and reported a state-of-the-art performance. The precision, recall and F1-score were 91.82%, 90.44% and 91.01%, respectively. Finally, through the comparative experiments with other self-constructed dataset (dish dataset), the results showed that the model had a certain generalization ability.