Development and Implementation of English Learning Resource Recommendation System based on Text Mining

In the current era of information technology, students are exposed to more diverse learning materials in the online environment, which makes the selection of English learning resources a challenge. This study aims to develop a personalized English learning resource recommendation system based on CNN-LSTM model through deep learning techniques, especially text mining. We collected a large amount of English learning resource data from the web and performed preprocessing and feature engineering to train the CNN-LSTM model. The experimental results show that the system performs well with an AUC value of 0.92 and high accuracy, and the user satisfaction survey also shows high user acceptance. In the future, we plan to extend this system to other language learning domains to verify its wide applicability.