A Study on the Integration of Online Classroom Teaching Resources in Universities Under the “Dual Carbon” Goals

In the context of achieving the “dual carbon” goals, integrating online educational resources in universities presents a unique opportunity to enhance sustainability while promoting energy-efficient practices. Motivated by the urgent need to optimize resource utilization and reduce carbon footprint in the educational sector, this study introduces a novel integration method for online classroom teaching resources, leveraging an advanced multi-label classification algorithm through Graph Neural Networks (GNNs). Our approach significantly improves the precision of resource categorization, facilitating more effective recycling and reducing energy consumption associated with digital learning platforms. The design of the algorithm specifically addresses the dual goals of maximizing resource reuse and minimizing energy consumption by optimizing data processing and storage strategies. Through rigorous experimentation, including comparative analysis with existing methods, our results excel in classification accuracy, resource efficiency and energy savings. This research not only offers a practical framework for sustainable online educational resource management but also contributes to the broader goal of reducing the environmental impact of digital education systems, aligning with global carbon reduction initiatives.