Research on the Evaluation of Credit Risk in Enterprises of the New Energy Vehicles Industry Supply Chain Based on the Integration of Knowledge Graph and KMeans Model

Aiming at the problem of increasing credit risk of enterprises in the new energy vehicles supply chain, this paper proposes a dynamic evaluation framework integrating knowledge graph and KMeans model. By integrating multi-source heterogeneous data to construct a knowledge graph, quantifying the risk transmission paths in the supply chain, and combining indicators such as enterprise finance and industry characteristics, the listed companies are classified into low, medium, and high-risk categories using the KMeans Model. The empirical results show that the contour coefficient and clustering effect of the model are significantly better than those of hierarchical clustering and GMM model, which reveals that high-risk enterprises are affected by financial and debt paying ability, and low-risk enterprises have outstanding supply chain stability. This study provides a dynamic early warning tool for financial institutions, helps enterprises optimize risk control strategies, and has practical value for industrial credit risk prevention and control.