中草药

基于自然语言处理和深度学习模型的中药黄芪主题建模分析

PDF下载

中草药 | 数据挖掘与循证医学 2026,57(17): 6887-6900

收起

中草药 |数据挖掘与循证医学 2026 , 57 (17) : 6887 -6900

基于自然语言处理和深度学习模型的中药黄芪主题建模分析

全屏

作者信息

通讯作者:

郑文江

作者简介:

李建阳: 李建阳，男，主要研究中西医结合防治呼吸系统疾病的临床和基础研究。E-mail:ljyskywalker@163.com

Topic modeling analysis of Astragali Radix based on natural language processing and deep learning models

Affiliations

doi: 10.7501/j.issn.0253-2670.2026.17.020

文章导航

摘要

收起

目的基于自然语言处理和深度学习语义建模方法，分析黄芪文献的研究现状、主题结构及演化特征，为黄芪研究热点识别和知识结构梳理提供参考。方法检索PubMed、Web of Science、Embase、中国知网、万方数据和维普资讯数据库中黄芪相关文献，检索字段限定为题名和关键词。经NoteExpress去重及人工筛选后，以题名/标题、关键词和摘要构建联合文本，采用文本清洗、术语规范化和语义嵌入等自然语言处理方法，结合PubMedBERT与BERTopic模型进行主题识别、可视化分析和Topic 0子主题分析。结果最终纳入中文文献12 159篇，英文文献1 536篇。中文文献2015年后发文量明显增加，英文文献总体呈持续增长趋势。主题建模结果显示，中文文献形成19个主要主题，主要涉及黄芪免疫调节、肿瘤免疫调控、改善周围神经病变、糖尿病肾病防治等药理作用，以及黄芪甲苷质量控制和磷脂酰肌醇-3-羟激酶（phosphatidylinositol-3-hydroxykinase，PI3K）-蛋白激酶B（protein kinase B，Akt）通路机制等方向；英文文献形成20个主要主题，主要涉及黄芪方药机制、质量分析、化学成分以及与癌症、纤维化、缺血再灌注损伤、糖尿病及肠道微生态相关的研究方向。Topic 0子主题分析显示，黄芪相关中文文献核心主题呈现“成分-制剂-复方-疾病-机制”交织特征，英文文献核心主题更突出机制化和模型化表达。结论黄芪的中英文研究均呈现持续增长和多主题分化趋势，中文文献更侧重临床应用、复方制剂、糖尿病肾病和质量控制，英文文献更侧重分子机制、网络药理学、天然产物化学和现代药理验证。自然语言处理与深度学习主题建模方法可有效揭示黄芪研究的知识结构和演化路径，为后续研究选题和证据整合提供参考。

关键词

黄芪 / 自然语言处理 / 深度学习 / PubMedBERT / BERTopic / 主题建模 / 糖尿病肾病

Abstract

收起

Objective To analyze the research status, thematic structure and evolutionary trends of Chinese and English literature on Huangqi (Astragali Radix) using natural language processing and deep-learning-based semantic modeling, and to provide references for hotspot identification and knowledge structure mapping. Methods Literature related to Astragali Radix was retrieved from PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Data and VIP databases, with the retrieval fields limited to titles and keywords. After deduplication using NoteExpress and manual screening, titles, keywords and abstracts were combined to construct the analytical corpus. Natural language processing approaches including text cleaning, terminology normalization and semantic embedding were performed, and PubMedBERT combined with BERTopic was used for topic identification, visualization and subtopic analysis of Topic 0. Results A total of 12 159 Chinese publications and 1 536 English publications were included. The publication output of Chinese publications increased markedly after 2015, while English publications showed a continuous upward trend. Topic modeling identified 19 major topics in Chinese literature, mainly involving immunomodulation, diabetic nephropathy, quality control of astragaloside IV, peripheral neuropathy, tumor immunoregulation and phosphatidylinositol-3-hydroxykinase (PI3K)-protein kinase B (Akt)-related network pharmacology. In English literature, 20 major topics were identified, mainly involving formula-related mechanisms, quality analysis of Astragali Radix, network pharmacology and cancer, chemical constituents, fibrosis, ischemia-reperfusion injury, diabetes and gut microbiota. Subtopic analysis of Topic 0 suggested that the Chinese core topic showed an intertwined pattern of “components-preparations-formulas-diseases-mechanisms”, whereas the English core topic was more mechanism- and model-oriented. Conclusion Both Chinese and English Astragali Radix literature showed continuous growth and thematic diversification. Chinese studies focused more on clinical application, compound formulas, diabetic nephropathy and quality control, whereas English studies emphasized molecular mechanisms, network pharmacology, natural product chemistry and modern pharmacological validation. Natural language processing and deep-learning-based topic modeling can effectively reveal the knowledge structure and evolutionary trajectory of Astragali Radix research, providing references for subsequent research topic selection and evidence integration.

Key words

Astragali Radix / natural language processing / deep learning / PubMedBERT / BERTopic / topic modeling / diabetic nephropathy

引用本文

李建阳, 杨移佳, 王骜, 彭焕蕊, 曾晓芳, 梁京京, 张伟, 郑文江, 里自然. 基于自然语言处理和深度学习模型的中药黄芪主题建模分析. 中草药, 2026 , 57 (17) : 6887 -6900 . DOI: 10.7501/j.issn.0253-2670.2026.17.020

LI Jianyang, YANG Yijia, WANG Ao, PENG Huanrui, ZENG Xiaofang, LIANG Jingjing, ZHANG Wei, ZHENG Wenjiang, LI Ziran. Topic modeling analysis of Astragali Radix based on natural language processing and deep learning models[J]. Chinese Traditional and Herbal Drugs, 2026 , 57 (17) : 6887 -6900 . DOI: 10.7501/j.issn.0253-2670.2026.17.020

基金

收起

广州市科技局基础研究计划 (2023A03J0306); 广州市科学技术局青年博士“启航”项目 (2025A04J3795); 2026年广州中医药大学“揭榜挂帅”青年拔尖人才团队项目 (郑文江)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

Liu Y X, Song X M, Dan L W, et al. Astragali Radix: Comprehensive review of its botany, phytochemistry, pharmacology and clinical application [J]. Arch Pharmacal Res, 2024, 47(3): 165-218.
Wang P P, Wang Z, Zhang Z P, et al. A review of the botany, phytochemistry, traditional uses, pharmacology, toxicology, and quality control of the Astragalus memeranaceus [J]. Front Pharmacol, 2023, 14: 1242318.
Zhang Y Y, Chen Z J, Chen L P, et al. Astragali Radix (Huangqi): A time-honored nourishing herbal medicine [J]. Chin Med, 2024, 19(1): 119.
Jin X J, Zhang H J, Xie X R, et al. From traditional efficacy to drug design: A review of Astragali Radix [J]. Pharmaceuticals, 2025, 18(3): 413.
Li S, Hu X P, Liu F, et al. Bioactive components and clinical potential of Astragalus species [J]. Front Pharmacol, 2025, 16: 1585697.
Liang Y T, Chen B Q, Liang D, et al. Pharmacological effects of astragaloside IV: A review [J]. Molecules, 2023, 28(16): 6118.
Wang B, Wu B L, Ma Y J, et al. Astragalus polysaccharides: Structure-immunomodulation relationships, multi-target pharmacological activities, and cutting-edge applications in immune modulation [J]. Front Immunol, 2025, 16: 1714898.
Shi Y S, Ma P. Pharmacological effects of Astragalus polysaccharides in treating neurodegenerative diseases [J]. Front Pharmacol, 2024, 15: 1449101.
Liu S Y, Wang L Y, Zhang Z H, et al. The potential of Astragalus polysaccharide for treating diabetes and its action mechanism [J]. Front Pharmacol, 2024, 15: 1339406.
Yang Q, Meng D D, Zhang Q Y, et al. Advances in research on the anti-tumor mechanism of Astragalus polysaccharides [J]. Front Oncol, 2024, 14: 1334915.
Wei X N, Leng X H, Liang J W, et al. Pharmacological potential of natural medicine Astragali Radix in treating intestinal diseases [J]. Biomed Pharmacother, 2024, 180: 117580.
Liu M F, Di Y M, May B, et al. Renal protective effects and mechanisms of Astragalus membranaceus for diabetic kidney disease in animal models: An updated systematic review and Meta-analysis [J]. Phytomedicine, 2024, 129: 155646.
Liu P Y, Hong K F, Liu Y D, et al. Total flavonoids of Astragalus protects glomerular filtration barrier in diabetic kidney disease [J]. Chin Med, 2024, 19(1): 27.
Chen Y J, Xie M R, Zhou S Q, et al. Research state of the herbal medicine Huangqi (Radix Astragali): A global and bibliometric study [J]. Medicine, 2024, 103(8): e37277.
Zheng Y J, Ren W Y, Zhang L N, et al. A review of the pharmacological action of Astragalus polysaccharide [J]. Front Pharmacol, 2020, 11: 349.
Gu Y, Tinn R, Cheng H, et al. Domain-specific language model pretraining for biomedical natural language processing [J]. ACM Trans Comput Healthcare, 2022, 3(1): 1-23.
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding [A] // Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies [C]. Minneapolis: Minnesota Association for Computational Linguistics, 2019: 4171-4186.
Lee J, Yoon W, Kim S, et al. BioBERT: A pre-trained biomedical language representation model for biomedical text mining [J]. Bioinformatics, 2020, 36(4): 1234-1240.
Beltagy I, Lo K, Cohan A. SciBERT: A pretrained language model for scientific text [A] // Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) [C]. Hong Kong: ACL, 2019: 3613-3618.
Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks [A] // Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) [C]. Hong Kong: Association for Computational Linguistics, 2019: 3982-3992.
Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation [J]. J Machine Learning Res, 2003, 3: 993-1022.
Grootendorst M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure [J/OL]. arXiv (2022-03-11) [2026-07-07]. https://arxiv.org/abs/2203.05794.
McInnes L, Healy J, Melville J. UMAP: Uniform manifold approximation and projection for dimension reduction [J/OL]. arXiv (2018-02-09) [2026-07-07]. https://arxiv.org/abs/1802.03426.
McInnes L, Healy J, Astels S. Hdbscan: Hierarchical density based clustering [J]. J Open Source Softw, 2017, 2(11): 205.
Lyu D M, Wang X B, Chen Y, et al. Language model and its interpretability in biomedicine: A scoping review [J]. iScience, 2024, 27(4): 109334.
Chen Q Y, Hu Y, Peng X Q, et al. Benchmarking large language models for biomedical natural language processing applications and recommendations [J]. Nat Commun, 2025, 16: 3280.
Usuyama N, Wong C, Zhang S, et al. Biomedical natural language processing in the era of large language models [J]. Annu Rev Biomed Data Sci, 2025, 8: 471-490.
Blei D M, Lafferty J D. Dynamic topic models [A] // Proceedings of the 23rd International Conference on Machine Learning-ICML '06[C]. Pittsburgh: ACM, 2006: 113-120.
Ma L, Chen R, Ge W G, et al. AI-powered topic modeling: Comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women [J]. Exp Biol Med, 2025, 250: 10389.
Han X, Huang L M, Li G, et al. Effect of Astragalus injection on left ventricular remodeling in HFmrEF: A systematic review and Meta-analysis [J]. Front Cardiovasc Med, 2024, 11: 1374114.

2026年第57卷第17期

PDF下载

引用本文

BibTeX

文章信息

doi: 10.7501/j.issn.0253-2670.2026.17.020

接收时间：2026-05-17
首发时间：2026-10-29

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2026-05-17

基金

作者信息

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/zcy/CN/10.7501/j.issn.0253-2670.2026.17.020

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT