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<p>The National Equities Exchange and Quotations (NEEQ) in China is a key platform for small and medium-sized enterprises (SMEs) to access public capital markets. However, their credit evaluation is challenging due to financial opacity and information asymmetry. Given that conventional credit evaluation methods mainly rely on financial statements, news reports, and transaction history, often overlooking the complex relationships that affect SMEs’ performance, we propose a KG-AttRGCN-XGBoost model to evaluate enterprise credit effectively. This model uses a knowledge graph (KG) to construct enterprise relationship networks, utilizes contextual embeddings of a relational graph convolutional network (RGCN) with a constrained attention mechanism to model complex inter-enterprise connections, and transmits the extracted features to XGBoost for credit evaluation. Experimental results show that our model significantly outperforms several popular graph neural network-based credit evaluation methods, better handling multi-relational data and achieving higher precision.</p>

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Mapping creditworthiness for Chinese small and medium-sized enterprises: integrating knowledge graphs and graph neural networks

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Cuixia Jiang,
  • Jingyuan Zheng,
  • Qifa Xu

摘要

The National Equities Exchange and Quotations (NEEQ) in China is a key platform for small and medium-sized enterprises (SMEs) to access public capital markets. However, their credit evaluation is challenging due to financial opacity and information asymmetry. Given that conventional credit evaluation methods mainly rely on financial statements, news reports, and transaction history, often overlooking the complex relationships that affect SMEs’ performance, we propose a KG-AttRGCN-XGBoost model to evaluate enterprise credit effectively. This model uses a knowledge graph (KG) to construct enterprise relationship networks, utilizes contextual embeddings of a relational graph convolutional network (RGCN) with a constrained attention mechanism to model complex inter-enterprise connections, and transmits the extracted features to XGBoost for credit evaluation. Experimental results show that our model significantly outperforms several popular graph neural network-based credit evaluation methods, better handling multi-relational data and achieving higher precision.