Compared to traditional supply chains, the large-scale, digitalized industrial interconnected intelligent manufacturing supply and demand network has stronger response and adjustment capabilities as well as risk prevention and recovery capabilities. However, it also faces a greater variety of risks and has broader risk transmission pathways, more easily threatening its robustness. Accurately describing the dynamic propagation process of fault risks within the network is fundamental to enhancing its robustness. Firstly, a model of the industrial interconnected intelligent manufacturing supply and demand network with multiple industrial communities is constructed. Secondly, by considering the relative correlation between business nodes, a risk propagation model with relative fault probabilities is built. Then a fault recovery model that considers both the recovery probability and recovery period is established based on the importance of nodes. Finally, a network is constructed based on an improved gravity model, and the relative connectivity rate R of the network is used as an indicator to simulate and analyze cascading failures under different fault and recovery scenarios. The simulation results indicate that there are critical values in all four sets of different fault and recovery scenarios that lead to R being in an unstable state in the long run. The parameters η and μ have certain marginal effects on the value of R. When the network’s fault propagation capability is fixed, the weaker the recovery capability, the more pronounced the oscillation of R, and the larger the scale of network impact. Conversely, with a certain recovery capability, the stronger the fault intensity, the more pronounced the oscillation of R, and the larger the scale of network impact.