Construction of an English Resource Library Network Information Recommendation Model Based on LSTM Algorithm

With the rapid development of information technology and the trend of digitization of educational resources, the library of English teaching resources is increasing, and how to efficiently utilize these resources to provide users with accurate recommendations has become a major challenge in the field of educational technology. First, collecting and organizing users’ historical browsing data and English education content in the resource library; second, using these data to train the LSTM model to capture the time-series features in users’ behaviors, as well as the dynamic changes of the content; and then analyzing the users’ potential needs through the model to achieve personalized recommendation. New users’ satisfaction with the LSTM system increased from 6 to 8, which suggests that the new system may have improved the new users’ experience through more accurate recommendations. This research not only has a guiding significance for resource management in educational institutions, but also provides a theoretical basis and a practical guide for the development and optimization of educational resources in the future.