Abstract: To address the challenges of acquiring water-richness information in surrounding rock during tunnel construction and the lack of timely early warning for water-inrush risks, this study proposes a digital twin and risk decision-making framework for evaluating the water-richness of surrouding rock ahead of tunnel face. The framework achieves virtual mapping of rock water-richness through point cloud modeling and introduces a fuzzy cloud probability model for refined risk assessment of water-inrush per meter. The framework comprises three key steps: (1) Acquisition of rock resistivity data via transient electromagnetic method (TEM) with 3D spatial registration to construct a water-richness database; (2) Generation of forward point cloud models based on tunnel face geometry, where Knearest neighbors (KNN) algorithm assigns water-richness attributes to establish a digital twin of water-richness of surrounding rock; (3) Uncertainty quantification of the digital twin using the fuzzy cloud probability model for intelligent water-inrush risk assessment. Application in Yanjiazhai Tunnel demonstrates that the framework enables rapid extraction of water-richness information and virtual mapping through digital twins. Comparative analysis with actual water-inrush conditions during excavation verifies the accuracy of the proposed fuzzy cloud probability model in risk evaluation of water inrush ahead of tunnel face.