## 2 Related Work
[PERSON], [PERSON] et al. [15] used a CRF approach for NER using TCM medical cases, and the results showed that the CRF approach was significantly better than the maximum entropy and SVM approaches. [PERSON] [16] et al. used CRF as the Chinese NER model, comparing two different levels of models based on character level and word level. Traditional machine learning methods convert NER tasks into sequence annotation problems, requiring a large amount of feature engineering. Due to the limitations of manually designed features, when the dataset is large, it is easy to generate over-fitting problems. The NER tasks in the field of shipping enterprise credit belong to domain-specific NER, and many long entities and complex entities exist in the shipping enterprise credit dataset, which require a large amount of domain knowledge as well as the need to manually design and extract a large number of features using the traditional machine learning approach, increasing the labor cost and time cost. In recent years, with the rise of deep learning, scholars have started to apply deep learning to NER tasks. The NER method based on deep learning can automatically extract data features and complete classification and train the model in an end-to-end manner. This approach does not require a lot of time and labor costs to design and extract features and can automatically extract features for each word or phrase. In addition, since deep learning is a data-driven approach, the larger the amount of data, the more accurately the model is trained and the better the entity recognition. Therefore, the use of a deep learning-based approach can effectively solve the problems of credit entity recognition of shipping companies with traditional machine learning. [PERSON] et al. [17] systematically compared the performance of multiple neural network models and proposed the Bidirectional Long Short-Term Memory (BiLSTM) and CRF model for three tasks of sequence annotation, pos annotation, chunking, and NER experiments on three different datasets, respectively, and the BiLSTM-CRF model had the best or near-best accuracy compared to the baseline model. [PERSON] and [PERSON] [18] combined BiLSTM, CNN, and CRF to propose a novel neutral network architecture that utilizes both word- and character-level representations without feature engineering or data preprocessing and is therefore suitable for a wide range of sequence labeling tasks, and the model achieved the best performance in experiments on both datasets. [PERSON] [19] et al. proposed a BiLSTM-IDCNN-CRF model of word embedding for an ecological governance technology domain dataset, which is obtained by a BiLSTM network and IDCN network to different granularity of features, and the experimental results verified the effectiveness of the word embedding method. According to the characteristics of data in the field of finance and taxation, [PERSON] [20] et al. proposed a BiLSTM-CRF model combining character features and word vector features to identify entity boundaries, using an integrated learning approach for entity type labeling and entity class labeling as a multi-label multi-category classification task, and the experimental results show that the combined word vector approach had higher recognition performance for entities of longer length and rare entities. [PERSON] [21] et al. proposed a BiLSTM-CRF model based on pre-trained word vectors to compensate for the lack of word vector specificity by adding word dynamic features and word character level features to improve the recognition level of complex multi-word meanings in geological texts and the extraction ability of local features of geological entities, and the experimental results proved to be better in the recognition of geological entities in small-scale corpus. [PERSON] [22] et al. proposed a two-level annotation model based on the combination of domain dictionary and CRF to recognize four types of entities: disease, symptom, drug, and operation. However, the word vector generated by Word2 Vec is static and cannot solve the problem of polysemy.