Optimization Study of Multi-Intelligent Economic Management Body System Modules in Adaptive Behavioral Pattern Learning and Simulation

As the operational landscape of enterprises grows increasingly complex and volatile, the significance of modularization in economic management has become even more pronounced. By segmenting the management system into distinct yet interdependent modules, enterprises are better equipped to adapt swiftly to market fluctuations, enabling the efficient allocation of resources and the enhancement of management efficacy. Enterprise risk management, a pivotal component of modular management, faces unprecedented challenges, with traditional risk assessment methodologies often failing to meet the stringent demands for precision and real-time responsiveness. To overcome these challenges, this paper proposes a novel GT-DQN framework, integrating Graph Neural Networks (GNNs), transformer, and Deep Q-Network (DQN) algorithms to facilitate risk assessment within enterprise economic management. The framework undertakes comprehensive modeling of enterprise financial data, market transaction records, macroeconomic indicators, and supply chain relationships via GNN, while the transformer captures dynamic shifts in time series data. Ultimately, DQN optimizes risk decision-making strategies within an evolving economic environment, thereby enhancing the accuracy and stability of risk assessments. Experimental results demonstrate that the GT-DQN framework developed in this study achieves a recognition accuracy of 90% on public datasets across three tiers of enterprise risk — high, medium, and low — providing a robust technical foundation for future risk prediction and analysis in the modular management of enterprise economies.