Heterogeneous Graph Neural Networks excel in various recommendation scenarios by effectively modeling and leveraging diverse information. However, two key challenges remain. First, heterogeneous information, such as user-item interactions, social relationships, and category tags, is found in distinct semantic spaces. Directly merging this information can lead to semantic confusion, making it difficult for the model to differentiate between relationships and reducing recommendation accuracy. Second, each type of heterogeneous relationship contains unique semantic characteristics. Current methods often focus solely on connectivity, neglecting these unique semantics, which limits the model’s ability to understand and represent heterogeneous information effectively. To address these challenges, we propose a novel approach named Multi-view Heterogeneous Graph with Cross-view Projection (MHGCP). This approach creates independent views for each heterogeneous semantic type to mitigate semantic confusion. Additionally, it introduces a cross-view projection layer that facilitates information transfer between semantic views and encodes inter-view relationships, allowing the model to indirectly capture the unique properties of each view. We tested our model on three real datasets, demonstrating superior performance. Through ablation studies and case studies, we validated the contribution of key modules in our approach to performance improvement. The implementation of the model can be found on https://github.com/fxl951677676/MHGCP .