Research Review on Named Entity Recognition of Ancient Chinese Medicine Books Based on Deep Learning

Corresponding author: shenghui, shenghui2217@163.com

DOI: 10.12201/bmr.202602.00050

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  Abstract: Purpose/Significance This review aims to systematically organize studies on deep learning-based named entity recognition(NER) in traditional Chinese medicine (TCM )ancient texts and offer insights for future research in this field. Method/Process This review employs a literature survey methodology. The core challenges and key technical framework for NER in TCM ancient texts are systematically analyzed. Furthermore, the research advances brought by deep learning in this domain are summarized, and future research prospects are outlined. Result/Conclusion Future research should promote the establishment of annotation standards and the construction of high-quality corpora. At the algorithmic level, efforts should strengthen data optimization in low-resource scenarios, complex entity recognition, and model interpretability to improve the accuracy and generalization capability of NER in TCM ancient texts.

  Key words: ancient traditional Chinese medicine texts; named entity recognition(NER); deep learning; natural language processing(NLP); review

  Submit time: 11 February 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2025-12-01	10.12201/bmr.202602.00050V1	Download

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