Title: A study on the extraction of topic keywords from English test questions text based on genetic algorithm

Authors: Yixian Lyu

Addresses: Foreign Language Department, Jiangxi Teachers College, Yingtan, 335000, China; Institute of Education and Humanities, University of Wales Trinity Saint David, Lampeter, SA48 7ED, UK

Abstract: In order to improve the efficiency and accuracy of topic keyword extraction, a genetic algorithm combining word frequency and inverse document frequency is studied, and a weight evaluation model is constructed using multi-threaded computation and text processing to improve algorithm efficiency. Comparing the performance of different algorithms, it was found that the current research algorithm has an accuracy of 98.9% and a runtime of 0.038 s, while the deep learning method has an accuracy of 97% and a runtime of 0.085 s. Therefore, the research results have demonstrated that the topic word extraction method based on a genetic algorithm for English composition texts not only improves the computational accuracy of topic word extraction but also has good application effects on word frequency extraction in English test questions and provides rich text vocabulary for vocabulary analysis in English test questions and learning platforms, thus having good application potential in the field of English test questions.

Keywords: English test questions; topic keyword extraction; genetic algorithm; feature item weights; text; similarity.

DOI: 10.1504/IJBIC.2026.153433

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.3, pp.192 - 202

Received: 28 Mar 2024
Accepted: 23 Jan 2025
Published online: 07 May 2026 *

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