The platform needs to design a reasonable resource recommendation mechanism to push learning resources and services that are selected, suitable, and satisfactory to users based on their personalized information. In this paper, we aim to build an AI-based personalized English learning recommendation platform, which adopts a self-attention mechanism to capture long-term dependencies in user learning data from a dialogue-based perspective, and uses position encoding and residual connection to enhance the expression of the model. Connection to enhance the expressive ability of the model. The system as a whole adopts the B/S architecture and uses Mysql and mongodb databases to build a front-end and back-end separated database. The final experimental results show that the new system significantly outperforms the old system in terms of click rate, recall rate, learning efficiency, user experience, user satisfaction, and user participation, which proves the effectiveness of this paper in combining AI algorithms to optimize the English learning recommendation system.
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