Objective

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To conduct a bibliometric analysis and evaluation of domestic and international literature published in the last 20 years on the application of natural language processing （NLP） technologies in the identification and labelling of traditional Chinese medicine （TCM） terminology， and explore the application and future development prospects of NLP technologies in the standardization research of TCM terminology.

Methods

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Literature from January 2003 to October 2023 was retrieved from databases including China National Knowledge Infrastructure （CNKI）， VIP Chinese Science and Technology Journal Database （VIP）， Wanfang Data Knowledge Service Platform （Wanfang）， China Biomedical Literature Service System （SinoMed）， and Web of Science. Data processing and statistical analysis tools such as Excel vba， Gephi， and PyCharm were used， employing bibliometric methods like frequency statistics， Apriori association analysis， and word cloud statistics to visually analyze the research hotspots.

Results

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①After screening， 442 papers met the research criteria， comprising 320 in Chinese and 122 in English. ②Publications showed a consistent growth trend from 2016 to 2023. ③The majority of publications were from China. ④Among the collected Chinese literature， a significant proportion of publications were master's and doctoral theses， with the highest number of publications from Beijing Jiaotong University. ⑤The China Academy of Chinese Medical Sciences had the highest publication count in Chinese， while University of Science and Technology Beijing led in English publications. There was frequent collaboration between TCM institutions and computer science institutions. ⑥The application of named entity recognition （NER） with BERT showed the most significant effects in TCM terminology research. ⑦Data related to TCM literature accounted for a substantial proportion.

Conclusions

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NLP-based research on the standardization of TCM terminology is still in the exploratory stage. Previous studies have shown the diversity of technology applications but lack systematization. Considering the potential of NLP technologies in TCM terminology recognition and labelling， further research is required to achieve systematic and intelligent TCM terminology standardization research and its widespread application.