The Internet has provided great convenience for the development of the education industry, but it has also brought about problems such as difficulties in selecting educational resources, difficulty in searching information, etc. To address these issues, the research constructs an educational resource Recommendation System (RS) grounded on Mahout Collaborative Filtering (CF) hybrid algorithm to provide efficient resource recommendations for users. During the construction of the system, the research also combines Multi-Dimensional Feature Fusion (MDFF) and deep learning personalized course recommendation methods to optimize courses and enhance the system’s ability to integrate multiple data. The experiment outcomes indicate that the hybrid algorithm has higher recommendation accuracy compared to CF algorithm, Fuzzy C-Means (FCM) algorithm, and the combination of knowledge graph completion and RS algorithm. The average recommendation accuracy of the four algorithms is 88.88%, 79.11%, 71.11%, and 65.53%, respectively. In addition, empirical analysis of the constructed educational resource RS reveals that the proposed hybrid algorithm has a lower Receiver Operating Characteristic (ROC) curve area value of 0.8984 and an F1-value of 0.8298, indicating good recommendation performance and superior performance compared to other comparative educational resource RSs. The above information indicates that the educational resource RS grounded on Mahout CF hybrid algorithm has certain stability and can offer customized suggestions for resources tailored to individual users in a timely manner. This research provides a practical method for online education on the Internet, which will help further improve online education in the education platform in the future.

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