Title: Multi-dimensional data mining of English online teaching platform based on improved decision tree

Authors: Jingping Du

Addresses: Faculty of Education, Beijing Normal University, Haidian, Beijing, China; School of Foreign Languages, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

Abstract: To improve the acceleration ratio and mining accuracy of data mining, a new multi-dimensional data mining method for English online teaching platforms is proposed based on improved decision tree. Information granule technology is introduced for data reconstruction, utilising neighbourhood data relationships to improve clustering accuracy. An association rule mapping structure is constructed using association matrix and difference coefficient matrix to present data set relationships, with mining factors and relative errors introduced to improve subsequent mining accuracy. The improved C4.5 decision tree algorithm is adopted, combined with principal component analysis to reduce dimensionality, and features are filtered through information gain rate to improve data mining accuracy and efficiency. Experimental results demonstrate significantly improved mining performance, with data mining acceleration ratio maintained above 0.9 and accuracy maintained above 98.54%.

Keywords: improve decision tree; English online teaching platform; multidimensional data; data mining.

DOI: 10.1504/IJCAT.2026.153104

International Journal of Computer Applications in Technology, 2026 Vol.78 No.3, pp.245 - 253

Received: 30 Dec 2024
Accepted: 10 Jun 2025
Published online: 22 Apr 2026 *

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