The convergence of large-scale digital recommendation systems and complex chemical manufacturing processes presents unprecedented challenges in resource allocation, system stability, and multi-objective optimization. This paper synthesizes recent advances across these domains, proposing an integrated framework that bridges heterogeneous resource slot optimization, predictive system safeguards, machine learning-driven logistics, knowledge graph-based audit systems, and multi-objective process regulation. By examining the cross-cutting principles of submodular optimization, resilience architecture, and collaborative control mechanisms, this study demonstrates how insights from digital platforms can inform industrial process optimization and vice versa. The framework emphasizes the importance of cross-space spillover effects, cascading resilience, and coupled regulation in achieving system-wide efficiency and stability.
