## 1 Introduction
For example, \"CFLP-AAA Logistics Enterprise\" is composed of \"C\", \"E\", \"L\", \"P\", \",\", \"AAA\", \"Logistics\", \"Enterprise\", \"CFLP-AAA\", and \"CFLP-AAA\". \"P\", \", \", \"AAA\", \"logistics\", and \"enterprise\" are combination of letters, words, and symbols. In the process of NER, it is difficult to accurately identify the entity if only a single feature vector is used as input for the model. 2. Entity nesting exists. For example, the company entity \"Anhui Eddyang County Shipping Company\" contains two entities: \"Anhui Province\" and \"Eddyang County\", so it is very easy to make a mistake in the process of entity recognition. Therefore, according to the textual characteristics of the shipping enterprise credit domain, this paper proposes the BERT-MF-BiGRU-CRF model for the NER task. The multi-features are integrated with the contextual features obtained from BiGRU and introduced into the CRF layer for classification. Compared with the model with a single feature, the model proposed in this paper can make full use of shipping enterprise credit text information for NER. The main contributions of the paper are as follows: first, the crawled shipping enterprise credit data are pre-processed, and the data are annotated by BIO. Next, multi-feature extraction is performed, and character-level features are extracted by the BERT model; the shipping enterprise credit word list is obtained by jieba word separation and removal of repeated words, and no additional user dictionaries are introduced in the word separation process. The words that all characters in the sentence may form are identified according to the generated word list, and then the characters are added to the four word sets B, M, E, and S according to their positions in the words, and when there is no matching word in the set, it is filled with the marker NULL. The four set representations corresponding to each character in the text sequence are counted, and the vectors of the four sets are stitched as the word vector representation corresponding to the character; the pos and word length of the words in the sets are counted, and the pos vector representation and the word length vector representation corresponding to each set are calculated by weighted average, and the word vector feature, pos feature, and word length feature are obtained, respectively. The extracted four types of features are stitched as the input of BiGRU, and the contextual features are extracted in BiGRU to obtain the text-rich semantic features. Finally, the sequence annotation is completed by CRF to fully explore the hidden information in the text labels and further improve the accuracy of shipping enterprise credit NER. The shipping enterprise credit entities recognition in this paper can also be used for relationship extraction to further lay the foundation for building a knowledge graph of shipping enterprise credit information. This paper is structured as follows: in Section 1, the background of credit entity recognition of shipping companies, data characteristics, and the contribution of this paper and the structure of the paper are briefly described. In Section 2, the current status of research on named entity recognition is described. In Section 3, the research content, the relevant theories, and methods used in this study are described. In Section 4, the data are pre-processed and annotated, and three types of experiments are conducted, which are the experimental comparison with existing research solutions, the experimental comparison including different feature models, and the experimental fine-grained entity experiment for shipping enterprise credit, and the following conclusion is drawn: the deep learning model based on multi-feature fusion proposed in this paper has the best NER effect on shipping enterprise credit. In Section 5, the main content of this paper is summarized, and the outlook of future research is outlined.