Deep Learning-Based Dynamic Graph Framework for Robust Corporate Financial Health Risk Prediction

This study proposes a dynamic graph enhancement framework designed for uncertainty conditions to address structural uncertainty, temporal nonlinearity, and multi-scale dependency in corporate financial health risk modeling, where accurately capturing the dynamic evolution of financial states under uncertainty has become a core scientific problem in the digital transformation of financial intelligence and corporate governance. The method starts from multi-source financial data and first performs multi-scale decomposition and normalization to achieve a hierarchical representation of financial time-series signals. Based on this, a dynamically updated enterprise correlation graph is constructed to capture potential risk transmission relationships among firms and to monitor industrial systematic risk. An uncertainty correction mechanism is then introduced to adaptively enhance the adjacency matrix, effectively mitigating modeling bias caused by data noise and structural perturbations. The main model adopts a time-aware graph convolutional network (T-GCN) to jointly learn temporal dependencies and graph topological features, combined with a contrastive learning constraint to strengthen feature consistency and the robustness of risk representation. Experimental results show that the proposed model significantly outperforms mainstream methods such as Transformer, GNN, GAT, and BiLSTM on multiple corporate financial datasets, achieving an average accuracy of 0.921 and improving the F1-Score by about 3.1%. These results verify its robustness and high-precision risk identification capability under dynamic uncertainty conditions. Overall, this study achieves dynamic modeling and structure-aware optimization of corporate financial health, providing an effective technical approach for financial risk monitoring, health assessment, and scenario analysis for financial forecasting and planning in complex economic environments.