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.

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