Abstract: The problem of modeling multi-sensor data fusion system under uncertainty is discussed. Taking advantage of the measure of relative proximity in terms of uncertainty, the authors firstly implement multi-sensor dynamic clustering. Based on Bayesian estimation technology and the measure of compatibility, an optimal fusion paradigm for multi-sensors data fusion in the same group is presented. By examining the mutual impact of sensor groups based on the measure of confidence, a novel model for dynamic multi-sensor fusion svstem is described. The efficient fusion of data from different sources enables the system to respond promptly to the uncertain environment. Finally, experiments demonstrate the model is of higher sensitivity and practicability, especially in uncertain environment for intelligent systems.