Research on the Design of Multimodal AI-Driven Visual Narrative Recommendation System for Cross-border E-commerce

In view of the complexity and diversity of user needs and cultural backgrounds in cross-border e-commerce scenarios, this paper proposes a theoretical framework of a multimodal artificial intelligence-driven visual narrative recommendation system, aiming to break through the information overload and cultural barriers of traditional recommendation systems through cross-modal semantic alignment, dynamic cultural adaptation and narrative logic generation. The study first integrates multimodal representation learning, visual narrative theory and cross-cultural consumer behavior model to construct a recommendation paradigm of “product-user-culture” ternary collaboration. By designing a cross-modal contrastive learning algorithm to solve the semantic gap between images and texts, introducing a cultural dimension-weighted attention mechanism to achieve dynamic localized adaptation of visual narratives, and transforming product features into an emotion-driven narrative logic chain based on a sequence generation model. Theoretical verification shows that the framework can significantly improve the long-tail exposure rate and user immersion of non-standard products.