Research on Online Business Foreign Language Course Resource Recommendation Algorithm Based on Neural Network

Aiming at the personalized recommendation problem of online business foreign language teaching resources, this paper proposes an innovative recommendation algorithm, which integrates Graph Convolutional Network (GCN) and Attention mechanism, aiming to optimize the acquisition process of foreign language learning materials for business students and improve learning efficiency and experience. This paper focuses on using GCN to capture the complex correlation structure between course resources, and strengthens the model's fine-grained understanding of user preferences and resource characteristics by means of the attention mechanism. GCN effectively extracts the implicit relationship between courses and user group phenomenon, and enhances the global perspective of the recommendation system. The attention mechanism ensures that the model can focus on the course features that users really care about, and improves the degree of personalization and satisfaction of the recommendation. The model shows better stability and adaptability when dealing with complex and dynamically changing recommendation scenarios. By visualizing the attention weight, the reason why the model makes a specific recommendation can be intuitively displayed, which increases the transparency and interpretability of the recommendation results.