Title: Study on deep mining of network ideological and political resources based on weighted deep forest

Authors: Dan Yin; Na Wang

Addresses: School of Architecture, Changchun University of Architecture and Civil Engineering, Chang'chun, 130607, China ' School of Management, Changchun University of Architecture and Civil Engineering, Chang'chun, 130607, China

Abstract: Traditional methods for deep mining of ideological and political resources on the internet have problems such as poor comprehensiveness in mining and ineffective fine-grained control. Therefore, a deep mining method of network ideological and political resources based on weighted deep forest is proposed. Firstly, the ideological and political resources are classified, and their relationships are structured into a tree-like structure. Then, keyword tables, index tables, and synonym index tables are designed to extract features of resources after indexing and dimensionality reduction. Finally, a multi-granularity scanning structure based on weighted deep forest is constructed to determine the existence status of resources on different network platforms, and resource deep mining is achieved by determining data weight factors. By analysing the experimental results, it can be seen that the method proposed in this article has good comprehensiveness in deep resource extraction, good fine-grained control effect, and good mining effect.

Keywords: weighted deep forest; network ideological and political resources; deep mining; tree-like structure; extract features; weight factors.

DOI: 10.1504/IJRIS.2025.148030

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.4, pp.281 - 290

Received: 25 May 2023
Accepted: 12 Jul 2023
Published online: 15 Aug 2025 *

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