Title: Retrieval method of network education resources based on associated data

Authors: Guojuan Li

Addresses: Department of Student Work and Social Sciences, Shijiazhuang University of Applied Technology, Shijiazhuang, 050081, China

Abstract: Aiming at the problems of low retrieval accuracy and long retrieval time in traditional online education resource retrieval methods, a method of online education resource retrieval based on associated data is proposed. First, establish a model of online education resources, obtain online education resource data through web crawlers, and then pre-process the obtained online education resource data through a series of steps. Then, based on the pre-processing results, construct a semantic grid computing model to extract semantic features of online education resources. Finally, based on semantic features, associate the semantic data of online education resources through semantic similarity and semantic correlation, Based on the associated data, a retrieval model for online education resources is established using naive Bayesian methods to obtain the retrieval results. Simulation results show that the proposed method has higher retrieval accuracy and shorter retrieval time for online education resources.

Keywords: associated data; network education; resource retrieval; web crawler; semantic grid; naive Bayes.

DOI: 10.1504/IJRIS.2025.147454

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.3, pp.163 - 172

Received: 28 Mar 2023
Accepted: 15 May 2023
Published online: 16 Jul 2025 *

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