Efficient prediction of default risk for bond-issuing enterprises is pivotal for maintaining stability and fostering growth in the bond market. Conventional methods usually rely solely on an enterprise's internal data for risk assessment. In contrast, graph-based techniques leverage interconnected corporate information to enhance default risk identification for targeted bond issuers. Traditional graph techniques such as label propagation algorithm or deepwalk fail to effectively integrate a enterprise's inherent attribute information with its topological network data. Additionally, due to data scarcity and security privacy concerns between enterprises, end-to-end graph neural network (GNN) algorithms may struggle in delivering satisfactory performance for target tasks. To address these challenges, we present a novel two-stage model. In the first stage, we employ an innovative Masked Autoencoders for Heterogeneous Graph (HGMAE) to pre-train on a vast enterprise knowledge graph. Subsequently, in the second stage, a specialized classifier model is trained to predict default risk propagation probabilities. The classifier leverages concatenated feature vectors derived from the pre-trained encoder with the enterprise's task-specific feature vectors. Through the two-stage training approach, our model not only boosts the importance of unique bond characteristics for specific default prediction tasks, but also securely and efficiently leverage the global information pre-trained from other enterprises. Experimental results demonstrate that our proposed model outperforms existing approaches in predicting default risk for bond issuers.