智能系统学报2025，Vol.20Issue(2)：457-464,8.DOI:10.11992/tis.202310039
基元潜部特征元挖掘的智能方法研究
Research on intelligent methods for latent features mining of basic element
摘要
Abstract
Latent feature element construction is a key aspect of the basic-element theory of extenics,and mining latent information is crucial for problem solving and fostering innovative thinking.This study explores the integration of the basic-element latent feature element manifestation theory with artificial intelligence algorithms to address the current problems of low efficiency,narrow coverage and the insufficient number of manually identified basic-element latent feature elements.A process-oriented,systematic method for mining latent feature elements of basic elements is pro-posed.The method involves using crawler technology to collect relevant information regarding target basic-element ob-jects,cleaning noisy data,and mining names and descriptions of constituent feature elements from sentences.A probab-ility statistical approach is then used to filter latent feature elements,with the intelligent mining process implemented through Python code.Finally,a case study comparison is performed to demonstrate the effectiveness of this approach.Research results can notably improve the recognition efficiency and intelligence level of basic-element latent feature ele-ments while also providing valuable insights for semantic generalization from complex and changeable dynamic corpus syntax.Additionally,it contributes to building a training set for enhancing the accuracy of intelligent extraction of fea-ture names and their quantitative values,thus promoting the development of extensible artificial intelligence theory.关键词
可拓学/潜部特征元/特征元/基元理论/人工智能/自然语言处理/大语言模型/可拓智能Key words
extenics/latent feature element/feature element/basic-element theory/artificial intelligence/natural lan-guage processing/large language model/extension intelligence分类
计算机与自动化引用本文复制引用
张丽芳,李兴森..基元潜部特征元挖掘的智能方法研究[J].智能系统学报,2025,20(2):457-464,8.基金项目
国家自然科学基金项目(72071049) （72071049）
广东省自然科学基金项目(2024A1515011324). （2024A1515011324）
