During the height of the COVID-19 epidemic, production lagged and enterprises could not deliver goods on time, which will bring considerable risks to the supply chain system. Modeling risk diffusion in supply chain networks is important for prediction and control. To study the influence of uncertain information on risk diffusion in a dynamic network, this paper constructs a dynamic evolution model based on a hypernetwork to study risk diffusion and control under uncertain information. First, a dynamic evolution model is constructed to represent the network topology, which includes the addition of links, rewiring of links, entry of nodes, and the exit of outdated nodes that obey the aging principle. Then, the risk diffusion scale is discussed with the Microscopic Markovian Chain Approach (MMCA), and the risk threshold is analyzed. Finally, the consistency of Monte Carlo (MC) simulation and MMCA is verified by MATLAB, and the influence of each parameter on the risk diffusion scale and risk threshold is tested. The results show that reducing the cooperation and production during the risk period, declining the attenuation factor, enhancing the work efficiency of the official media, and increasing the probability of the exit of outdated nodes in the supply chain networks will increase the risk threshold and restrain the risk diffusion.

The impact of COVID-19 is global, and uncertain information will affect product quality and worker efficiency in the complex supply chain network, thus bringing risks. Aiming at individual heterogeneity, a partial mapping double-layer hypernetwork model is constructed to study the supply chain risk diffusion under uncertain information. Here, we explore the risk diffusion dynamics, drawing on epidemiology, and establish an SPIR (Susceptible–Potential–Infected–Recovered) model to simulate the risk diffusion process. The node represents the enterprise, and hyperedge represents the cooperation among enterprises. The microscopic Markov chain approach (MMCA) is used to prove the theory. Network dynamic evolution includes two removal strategies: (i) removing aging nodes; (ii) removing key nodes. Using Matlab to simulate the model, we found that it is more conducive to market stability to eliminate outdated enterprises than to control key enterprises during risk diffusion. The risk diffusion scale is related to interlayer mapping. Increasing the upper layer mapping rate to strengthen the efforts of official media to issue authoritative information will reduce the infected enterprise number. Reducing the lower layer mapping rate will reduce the misled enterprise number, thereby weakening the efficiency of risk infection. The model is helpful for understanding the risk diffusion characteristics and the importance of online information, and it has guiding significance for supply chain management.