Title: Fast classification method for online vocational education resources based on big data decision tree

Authors: Ruiron Lan; Shanshan Yu

Addresses: School of Education, Huainan Normal University, Huainan, 232038, Anhui, China ' School of Digital Economy and Management, Guangdong Country Garden Polytechnic, Qingyuan, 511510, Guangdong, China

Abstract: To solve the problems of low efficiency and accuracy in the rapid classification of vocational education online resources, a fast classification method for vocational education online resources based on big data decision trees is proposed. Firstly, the effective information return state formula is used to filter valuable information, and wavelet transform is used to remove data noise. Secondly, the sliding window method is used to increase teaching resource data, calculate the mutual information value between features and learning objectives, and select high mutual information features to form the final feature set. Finally, using C4.5 decision trees, calculate information entropy, information gain, etc., to achieve effective classification of online vocational education resources. Experimental results have shown that the convergence speed of the classification method proposed in this paper is fast, consistently below 0.4 seconds, with an AUC area close to 1, and classification accuracy consistently above 90%.

Keywords: decision tree algorithm; vocational education; online resources; information entropy; information gain rate.

DOI: 10.1504/IJBIDM.2025.149081

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.346 - 362

Received: 23 Jan 2025
Accepted: 18 Jun 2025
Published online: 13 Oct 2025 *

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