A self-supervised graph embedding approach based on hierarchical projection network called Multi-view heterogeneous graph projection network(MeghenNet) was introduced to learn low-dimensional representations from multiple views, and the concept of multiple-view heterogeneous graphs was formalized for modeling heterogeneous graphs from various sources simultaneously. A hierarchical attention projection that involves a cross-relation projection to extract semantics information within each view was employed, followed by a cross-view projection to aggregate contextual information from other views. The mutual information between the view-specific embeddings and the corresponding high-level summary was computed to ensure the information consistency across views. Experimental results on several real-world datasets demonstrate that the proposed method outperforms state-of-the-art approaches when handling multi-view heterogeneous graphs.