农业大数据学报2026，Vol.8Issue(2)：155-162,8.DOI:10.19788/j.issn.2096-6369.000153
农业数据库文本多特征融合关键词抽取方法
Multi-feature Fusion Based Keyword Extraction Method for Agricultural Databases
摘要
Abstract
Automated keyword extraction from agricultural database texts is a crucial step in achieving intelligent utilization and services.This study addresses the challenges faced by traditional keyword extraction methods,which struggle to mine deep semantic associations within texts,as well as the issues with semantic embedding-based approaches that are susceptible to semantic representation bias and dilution of key information.By innovating on the model,we propose a more precise keyword extraction method tailored for agricultural database texts.We enhance the semantic associations and edge weight accuracy of the TextRank word graph by incorporating co-occurring word analysis and construct a feature statistics module to extract candidate keywords.Simultaneously,we integrate the Bert-base-Chinese pre-trained model for vectorized encoding of texts and extract candidate keywords through vector similarity calculations.Finally,a multi-source fusion decision-making process is employed to generate the final keyword list by fusing and weighting the outputs from the two modules,along with word positions,resulting in a keyword extraction method(BWE-COW-TR)that combines BERT semantic embedding with TextRank word graph and co-occurring word analysis features.The precision(49.83%),recall(58.29%),and F1 score(0.5373)obtained from keyword extraction experiments conducted on an agricultural science and technology literature dataset using this method are all significantly higher than those of the baseline model.Its F1 score has improved by 70.90%,51.74%,and 45.77%,respectively,compared to the F1 scores of KeyBERT,TF-IDF,and TextRank.The research results demonstrate that the proposed method outperforms the commonly used KeyBERT,TF-IDF,and TextRank methods in keyword extraction from agricultural database texts.关键词
农业信息/TextRank/BERT/共现词/关键词抽取/语义嵌入模型Key words
agricultural information/TextRank/BERT/co-occurrence/keyword extraction/semantic embedding model引用本文复制引用
杜若鹏,张洁,寇远涛..农业数据库文本多特征融合关键词抽取方法[J].农业大数据学报,2026,8(2):155-162,8.基金项目
科技部科技创新2030——新一代人工智能重大项目(2021ZD0113705),中国农业科学院科技创新工程大数据与知识服务创新团队项目(CAAS-ASTIP-2021-AI-06). （2021ZD0113705）
