This paper focuses on the symbiotic development of rural e-commerce and logistics by exploring the intersection of fusion modeling with public management. By leveraging data fusion and logistics optimization, we address the burgeoning needs of rural e-commerce, presenting a deep learning approach that incorporates Deep Belief Networks (DBNs) and weight-sharing to achieve a 40% data compression rate. This advancement significantly lowers storage and energy costs while enhancing computational efficiency. Furthermore, we devise a rural e-commerce logistics and distribution model optimized through an ant colony algorithm to maximize profits and demand coverage. The application of this Model has led to a 20% increase in profits and a surge in demand coverage to 90%, showcasing the efficacy of fusion models in streamlining rural e-commerce logistics. These insights contribute to the broader discourse on innovative public management strategies.