Enterprise economic audit and management increasingly require adaptive decision-support systems capable of operating with heterogeneous data, nonlinear constraints, distributed computing resources, and incomplete supervision. This study proposes a model-driven enterprise economic management framework that combines genetic-algorithm-based optimization, deep-learning decision support, weakly supervised learning, model transformation, and edge–cloud collaboration. The framework separates business-level models from platform-specific implementation through model-driven engineering and Query/View/Transformation (QVT)-style transformations, while Open Neural Network Exchange (ONNX)-compatible deployment is used to support model portability across heterogeneous execution environments. Genetic algorithms are employed for constrained resource-allocation and scheduling problems, whereas deep-learning models provide data-driven prediction and representation capabilities. Weak supervision and active learning are incorporated to reduce dependence on fully labeled enterprise datasets. At the infrastructure level, software-defined networking and stochastic network calculus support adaptive routing, distributed task placement, and probabilistic delay analysis across edge and cloud resources. The case-study evaluation reports a 32.6% improvement in data-flow efficiency after genetic-algorithm optimization, resource utilization of 91.6% for the GA-based strategy, and active-learning classification accuracy exceeding 63% when only 1% of the data are labeled. These results are treated as case-specific simulation outcomes rather than universal performance guarantees. The proposed framework contributes an integrated architecture for transforming enterprise economic models into deployable intelligent services while maintaining model traceability, resource efficiency, and responsiveness under distributed operating conditions.
