Intelligent Integration of Online Environmental Education Resources for English Language and Literature Majors Based on Collaborative Filtering Algorithm

There are currently many different types and dispersed online educational resources, which inconvenience users and result in a low utilisation rate of resources. As a result, a new approach is required to realise the integration and recommendation of educational resources. This paper examines the intelligent integration and recommendation of online learning resources for English language and literature majors based on CF. The development of online English language and literature education resources is currently in the process of being discussed, and some flaws in the process are being examined in this paper. The creation and incorporation of a network education resource database are proposed as some strategies and recommendations. The information entropy method is employed to address the cold start problem brought on by the data sparseness of new users and new projects in CF. While this is happening, the recommendation process's similarity algorithm is being enhanced. This algorithm's decision support accuracy has been found to be 96.01% after extensive testing. Its accuracy is roughly 8% better than that of conventional CF, which has a precision of 8%. The results demonstrated a degree of accuracy in the improved algorithm.