Article: Research on fuzzy clustering of ideological and political MOOC resources under the background of 'Internet plus' Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.9 pp.97 - 116 Abstract: The massive, diverse, and complex structure of ideological and political learning materials on MOOC platforms results in low clustering accuracy and high time consumption. This study introduces a novel approach for categorising digital educational resources for political education within MOOC platforms through advanced computational techniques. The methodology begins with comprehensive data acquisition using webbased harvesting tools, followed by rigorous data refinement procedures to ensure information integrity. Subsequently, an enhanced convolution neural network architecture incorporating document embedding vectors was developed for textual feature representation. The framework integrates kernelbased fuzzy cmeans clustering enhanced with bioinspired optimisation techniques, specifically employing echolocationinspired swarm intelligence as the initialisation mechanism for improved convergence. Empirical validation demonstrated superior performance metrics, achieving classification precision above 95% and retrieval effectiveness surpassing the 93% benchmark. Inderscience Publishers - linking academia, business and industry through research

Title: Research on fuzzy clustering of ideological and political MOOC resources under the background of 'Internet plus'

Authors: Dandan Liu

Addresses: School of Marxism, Zhengzhou Technology and Business University, Zhengzhou, 451400, China

Abstract: The massive, diverse, and complex structure of ideological and political learning materials on MOOC platforms results in low clustering accuracy and high time consumption. This study introduces a novel approach for categorising digital educational resources for political education within MOOC platforms through advanced computational techniques. The methodology begins with comprehensive data acquisition using webbased harvesting tools, followed by rigorous data refinement procedures to ensure information integrity. Subsequently, an enhanced convolution neural network architecture incorporating document embedding vectors was developed for textual feature representation. The framework integrates kernelbased fuzzy cmeans clustering enhanced with bioinspired optimisation techniques, specifically employing echolocationinspired swarm intelligence as the initialisation mechanism for improved convergence. Empirical validation demonstrated superior performance metrics, achieving classification precision above 95% and retrieval effectiveness surpassing the 93% benchmark.

Keywords: Internet plus; MOOC; ideological and political learning materials; fuzzy clustering.

DOI: 10.1504/IJBIDM.2026.154229

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.9, pp.97 - 116

Received: 14 Nov 2025
Accepted: 09 Mar 2026
Published online: 17 Jun 2026 *

Title: Research on fuzzy clustering of ideological and political MOOC resources under the background of 'Internet plus'

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