Research on Quality Evaluation of Ideological and Political Education Resources Based on Artificial Intelligence Technology

Abstract

The rapid expansion of digital ideological and political education resources has increased resource quantity and diversity, but has also produced problems such as value-orientation deviation, content homogenization, loose theoretical logic, uneven quality, and inconsistent manual evaluation. To meet the normalized quality evaluation needs of large-scale digital resources, this study proposes an artificial-intelligence-enabled evaluation framework. Large language model semantic analysis, deep learning feature extraction, analytic hierarchy process, and fuzzy comprehensive evaluation are integrated to build a multi-level indicator system covering political orientation, content quality, teaching adaptation, technical standards, communication experience, and compliance-homogenization control. The model supports intelligent collection, semantic mining, indicator scoring, comprehensive diagnosis, classification, and precise resource recommendation. It can improve evaluation efficiency while retaining expert-derived political and educational standards through procedural consistency. In engineering-supported education environments, the framework is also relevant to wireless learning platforms, electromagnetic information infrastructures, and secure data transmission systems that deliver ideological and political resources across campuses. The study provides methodological support for the quality governance, optimization, and standardized dissemination of digital educational resources.