关键词: 图神经网络, 大语言模型, 图结构感知, 企业风险预测, 图结构统一表示

Abstract:

During the process of enterprise investment attraction， there are multi-dimensional risks. Traditional risk assessment methods are difficult to identify these risks timely and accurately due to information distortion and complex relationships in economic behaviors. To address the above issues， a risk analysis framework integrating Large Language Model （LLM） and Graph Neural Network （GNN） was proposed. The semantic understanding capability of LLM was utilized to assist the GNN in constructing a more comprehensive and accurate dynamic heterogeneous knowledge graph of enterprises， thereby solving the information distortion problem caused by static data. On this basis， to address the shortcomings of GNN in terms of deep and semantic expression abilities， a knowledge-based semantic structure mining module was designed， and Qwen large model was combined to enhance the semantic accuracy of node representations. Furthermore， an Integrated One Graph （IOG） module was proposed to unify node classification and graph classification tasks into the prediction of “focus nodes”. Through a unified prediction mechanism， predictions for different graph structure types were achieved， thereby improving the model’s generalization ability on different datasets significantly. The IOG-CIQAN（In One Graph with Collective Intelligence and Qwen2 Assistance Network） model constructed on the basis of this framework achieved accuracy over 87% on all of three risk analysis datasets in labor， finance， and administration compared to multiple baseline models such as Capsule Network （CapsNet）.

Key words: Graph Neural Network (GNN), Large Language Model (LLM), graph structure awareness, enterprise risk prediction, unified graph structure representation