Intelligent instructional resource management incorporating emotional and semantic features of user comments

The proliferation of digital teaching resources has exacerbated challenges in personalised recommendation due to information overload. This study introduces an intelligent management framework that integrates emotional and semantic features extracted from user-generated comments. By employing an adaptive weighting mechanism, multimodal feature fusion is achieved by analysing emotional intensity in user comments and dependencies among educational entities, utilising an adaptive weighting mechanism. Experimental evaluations on the EdNet public dataset reveal a 12.7% improvement in recommendation accuracy and a 9.2% increase in F1-score. These enhancements not only significantly optimise the assessment of resource quality but also improve the delivery of personalised services, thereby underscoring the framework's effectiveness in advancing educational resource management. Furthermore, this approach addresses critical limitations in existing systems and provides scalable solutions suitable for real-world applications.