Faced with massive resources, many learners find it difficult to quickly screen out useful content for themselves. In order to help learners acquire the required network resources quickly and accurately, the birth of a personalized recommendation system solves this problem perfectly. A collaborative filtering algorithm has been widely used in the field of personalized recommendation. However, due to the limitation of the model, the recommendation effect has not been further improved. The single weakness of a collaborative filtering algorithm to recommend learning resources is difficult to meet the needs of learners to acquire personalized resources. This paper proposes a recommendation algorithm for business English online learning resources based on an attention mechanism and collaborative filtering model. The learner vector and learning resource vector are mapped to multispace, and the learner-learning resource interaction is done from multiple angles. The final learner representation vector and learning resource representation vector are aggregated by a two-level attention mechanism to predict scores. Through teaching practice in student associations, it is found that students from different backgrounds have different preferences for business English online learning resources. This method has a positive impact on online learning. This study aims to provide some references for English education resource recommendations. The results at Precision@K and Recall@K prove that the proposed model has better recommendation ability.