北京师范大学学报（自然科学版）2025，Vol.61Issue(6)：786-796,11.DOI:10.12202/j.0476-0301.2025148
基于图神经网络的企业财务困境预测方法
Graph neural network applied to predict corporate financial distress
摘要
Abstract
Two financial distress prediction frameworks integrating information from the time and spatial domains are proposed based on graph neural network(GNN).A class imbalance handling module is designed after fusion of financial data with patent information to enhance prediction accuracy.To evaluate this model,effectiveness of class imbalance handling module,synergistic values for incremental information of patent data,technological innovation information,and graph structure modeling are each verified.It is found that multi-graph convolutional recurrent network with the"spatial-first,temporal-later"information aggregation strategy delivers superior performance.The present work provides new strategies for financial distress prediction.关键词
财务困境预测/图神经网络/类别不平衡/专利Key words
financial distress prediction/graph neural network/class imbalance/patent分类
信息技术与安全科学引用本文复制引用
张涵,徐静蕾,俞睿桦,徐媛铃,赵昆,赵晓航..基于图神经网络的企业财务困境预测方法[J].北京师范大学学报（自然科学版）,2025,61(6):786-796,11.基金项目
国家自然科学基金资助项目(72401172) （72401172）
