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The specific tasks are as follows: (1) first\/, a data transfer method based on entity features is proposed. By calculating the similarity of feature distribution between low resource data and high resource data, the most representative entity features are selected for feature transfer mapping, and the distance of entity distribution between the two domains is calculated to make up the gap between the data of the two domains then model is trained by high resource data. (2) Then, an entity boundary detection method is proposed. This method utilizes the BiLSTM+CRF as the main structure and integrates character boundary information to assist the attention network to improve the model\u2019s ability to recognize entity boundaries. (3) Finally, multiple named entity recognition methods are selected as baseline methods for comparison, and experiments are conducted on several datasets. The results show that the model proposed in this paper improves the accuracy of named entity recognition by 1%, the recall rate by 2%, and the<jats:italic>F<\/jats:italic>1 value by 2% on average in the field with low-resource.<\/jats:p>","DOI":"10.1007\/s44196-023-00244-3","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T14:02:49Z","timestamp":1681826569000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Research Toward Chinese Named Entity Recognition Based on Transfer Learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Hui","family":"Kang","sequence":"first","affiliation":[]},{"given":"Jingwu","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Yunpeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Tie","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"244_CR1","unstructured":"Yadav, V., Bethard, S.: A survey on recent advances in named entity recognition from deep learning models (2019). arXiv preprint arXiv:1910.11470"},{"issue":"11","key":"244_CR2","first-page":"62","volume":"32","author":"L Guanghe","year":"2018","unstructured":"Guanghe, L., Shaowu, Z., Hongfei, L.: Named entity identification based on fine-grained word representation. 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