The rise of multi-industry chain networks has transformed traditional industrial operations into complex systems with intricate cross-layer resource dependencies and company group structures. Despite enhancing operational efficiency, these networks are increasingly vulnerable to task surges—sudden spikes in resource demand that propagate across layers, and trigger cascading failures. Existing emergency response strategies, designed for single-layer networks, fail to address the multidimensional challenges of maintaining critical company stability and ensuring overall network robustness. To address this gap, this paper introduces the Multi-Modal Collaborative (MMCol) algorithm, which incorporates: (1) A dual-dimension collaboration framework enabling coordination across intra-layer vs. inter-layer and single company vs. company group, (2) adaptive inter-layer collaboration coefficients for dynamic resource allocation and stability preservation, and (3) a hierarchical decision strategy that reduces computational complexity by treating company groups as collaborative units and by pre-selecting layers based on resource compatibility. Extensive simulations across various network scales and experiments on real industry chain datasets demonstrate that MMCol consistently outperforms state-of-the-art algorithms in key metrics including load balance, collaboration cost, and key entity risk protection. The performance advantages are particularly significant in complex networks that mirror real industrial structures, thereby confirming MMCol’s effectiveness in practical multi-industry chain environments.