Due to the impact of sudden events, the security of global supply chains is facing serious challenges. Modeling supply chain networks as complex networks and analyzing them can effectively identify key nodes, thereby providing support for downstream risk analysis. Currently, various methods have been proposed to identify influential nodes in complex networks by constructing network topological features. However, due to the unique characteristics of supply chain networks, there is a lack of targeted methods for identifying influential nodes in this field. To address this issue, this paper proposes a framework for identifying influential nodes in complex networks—DHCBR, based on ResNet and node feature representation. By analyzing supply chain networks, we propose using node degree, H-index, and clustering coefficient to characterize node information, and we evaluate the algorithm using the ResNet model. Experimental results show that DHCBR can effectively identify influential nodes in supply chain networks and has the potential to be generalized to other real-world networks. Furthermore, through a comparison of time overhead with other methods, DHCBR demonstrates good applicability when dealing with large-scale networks.