Construction and Sharing of Educational Digital Resources Based on Deep Learning Algorithm

The fast-evolving nature of digital technology has a significant impact on educational materials are created, managed, and shared. The research introduces a smart framework to build and share educational digital materials using a learning-driven auto-approach. The system proposed seamlessly combines multi-sourced educational content, adaptive classification, and customized access for learners and teachers. Through the use of computational models for knowledge extraction, content structuring, and semantic relevance evaluation, the framework not only improves the quality of digital learning assets but also makes more accessible. Experimental testing reveals that the system is very accurate in its classification and recommendation, with an accuracy rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{9 8. 4 \%}$</tex>, showing it has the ability to provide context-aware and relevant resources to target users. The results underscore the promise of smart digital resource platforms to enhance the effectiveness of teaching, enhance learner engagement, and facilitate collaborative sharing of knowledge among educational institutions.