Research on the Construction and Reasoning of Educational Knowledge Graph Based on Pre-trained Language Model

In this paper, we propose a comprehensive framework for educational knowledge graph construction and reasoning that integrates pre-trained language models with structured knowledge representation to address complex educational knowledge organization and inference challenges. Our approach employs a three-module architecture comprising hierarchical knowledge extraction, graph construction, and neural-symbolic hybrid reasoning components, specifically designed for educational domain applications. We conduct extensive experimental validation including systematic ablation studies, hyperparameter sensitivity analysis, and cross-domain generalizability evaluation on large-scale educational datasets. The framework achieves an F1-score of 0.853 in knowledge extraction and path accuracy of 0.786 in reasoning tasks, outperforming baseline methods by 8.2% and 8.5% respectively. Cross-domain evaluation demonstrates robust generalizability with 95.2% transfer efficiency when applied to specialized educational domains, while scalability analysis reveals favorable sub-linear growth characteristics suitable for institutional deployments. The comprehensive analysis confirms the framework's effectiveness in capturing complex educational relationships, generating adaptive learning paths, and supporting intelligent tutoring systems. These results establish the practical applicability of our approach for modern educational knowledge management, personalized learning applications, and educational technology systems, though broader validation across diverse educational contexts remains necessary for comprehensive deployment.