Digital education mining technology based on composite collaborative filtering and Eclat algorithm

The field of digital education is rapidly growing, demanding effective resource utilisation. Traditional collaborative filtering (CF) algorithms face challenges with large, complex datasets. This study addresses these limitations by integrating CF with association rule mining, using a novel IBCF-UBCF composite CF algorithm and Eclat technology. Data was collected from multiple sources and fused for enhanced educational mining. Results show Eclat outperforms apriori, reducing CPU usage by 55% and physical memory usage by 51.9%, while the composite filtering algorithm achieved over 99% accuracy. The Eclat-IBCF-UBCF algorithm offers robust support for digital education, advancing educational data mining and personalised recommendations. It is recommended for implementation in digital education systems due to its efficiency and accuracy. Further research should focus on enhancing and integrating this algorithm with other educational technologies.