Enterprise credit rating is crucial for risk management in the complex business environment. However, existing models encounter challenges in adeptly handling heterogeneous relationships and efficiently addressing risk propagation. This study introduces a novel framework for enterprise credit rating, the Risk Contagion Graph Neural Network (RCGNN), designed to overcome these challenges. The framework comprises four distinct modules: enterprise feature embedding, risk contagion intra-aggregation, risk contagion inter-aggregation, and credit rating prediction. Through the integration of advanced graph neural networks and attention mechanisms, RCGNN effectively captures intricate risk propagation relationships among enterprises. Extensive experiments were conducted on a self-constructed dataset of Chinese listed companies, and the results demonstrate that RCGNN outperforms traditional machine learning methods and homogeneous graph neural network models in credit rating tasks.