Structured State Representation and Constraint-Guided Policy Learning for Intelligent Business Decision Systems

To address the difficulty of maintaining stable structural modeling in intelligent decision systems under complex dynamic environments and the sensitivity of decision behavior to state uncertainty, this study proposes an intelligent decision modeling method based on structure aware learning. The method constructs structured state representations that map environmental observations, state evolution relationships, and decision processes into a unified representation space. This design enables explicit modeling of internal structural constraints and dynamic dependencies within the system. During decision modeling, structural constraints are introduced to guide policy learning, allowing decision behavior to remain consistent and stable under long term optimization objectives. The proposed framework effectively captures state evolution patterns in complex state spaces and mitigates decision instability caused by excessive reliance on local information in traditional methods. Comparative evaluations demonstrate clear advantages in decision utility, decision stability, state transition modeling accuracy, and representation consistency. These results indicate that structure aware learning provides a more reliable modeling foundation for intelligent decision systems. The findings further show that systematically integrating structural information into decision modeling improves overall performance and trustworthiness in complex dynamic scenarios.