Rotor systems are important parts of rotating machinery. Real-time health monitoring of rotor systems is essential for safe operation. Data-driven modeling based on sensor data is currently the focus of health monitoring, which solves the problem that traditional physical modeling cannot be applied to complex mechanical equipment. Parameters uncertainty of data-driven modeling is inevitable, and analyzing and utilizing this uncertainty is critical to improving the adaptability of data-driven monitoring methods. This article focuses on the uncertainty quantification of fault features under the uncertainty of data-driven model parameters. Combined with Monte Carlo simulation and Latin hypercube sampling, the influence mechanism of system performance degradation on the quantification of feature uncertainty is analyzed. Finally, a Jeffcott rotor test rig with a rub-impact device was built, and the feature uncertainty caused by the nonlinear autoregressive with exogenous input (NARX) model parameter uncertainty was explained. The experimental results show that the reliability of the proposed fault features based on the uncertainty is significantly better than the traditional signal-based features, which significantly improves the adaptability of the data-driven method in the health assessment of the rotor system.