Intelligent Construction of Web-Based Environmental Education System in Universities

This study develops an intelligent web-based environmental education system for universities to overcome traditional limitations, including inefficient knowledge dissemination, fragmented content, and limited interactivity. It innovatively applies the whale optimization algorithm to environmental education platforms by encoding multidimensional user features, such as learning behavior, interest tags, and academic background, to address cold-start and low-accuracy limitations of conventional recommendation methods. The system adopts a scalable architecture based on front-end and back-end separation with hybrid data storage, enabling cross-device accessibility and efficient management of structured and unstructured data. Experimental results demonstrate that whale optimization algorithm improves recommendation accuracy to 88.4%, compared with 75.6% for collaborative filtering, and increases cold-start accuracy for freshmen to above 78%. These findings confirm the practical value of swarm intelligence in educational applications and provide a replicable digital solution for advancing green campus development in higher education institutions.