[1]ZHANG Lifang,LI Xingsen.Research on intelligent methods for latent features mining of basic element[J].CAAI Transactions on Intelligent Systems,2025,20(2):457-464.[doi:10.11992/tis.202310039]
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Research on intelligent methods for latent features mining of basic element

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 457-464 Column: 学术论文—人工智能基础 Public date: 2025-03-05
Title:
Research on intelligent methods for latent features mining of basic element
Author(s):
ZHANG Lifang;  LI Xingsen
Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou 510006, China
Keywords:
extenics; latent feature element; feature element; basic-element theory; artificial intelligence; natural language processing; large language model; extension intelligence
CLC:
TP18
DOI:
10.11992/tis.202310039
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 proposed. The method involves using crawler technology to collect relevant information regarding target basic-element objects, cleaning noisy data, and mining names and descriptions of constituent feature elements from sentences. A probability 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 elements 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 feature names and their quantitative values, thus promoting the development of extensible artificial intelligence theory.
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