To address the issue that traditional fault diagnosis methods struggle to accurately diagnose faults in the nuclear reactor coolant system (RCS) of nuclear power plants under uncertain conditions, a dynamic fuzzy radial basis function neural network(DFRBFNN) model was established for RCS fault diagnosis following these steps. First, based on the fault types and sample data of the RCS, the initial structure of the DFRBFNN model was determined. Then, using the radial basis function neural network method, the initial DFRBFNN model for RCS fault diagnosis was constructed, and a random initialization method was applied to initialize the connection weights from the defuzzification layer to the output layer of the initial DFRBFNN model. Finally, the error reduction rate method was used to adjust the structure and parameters of the initial DFRBFNN model, resulting in the final DFRBFNN model for RCS fault diagnosis. The established model was applied to diagnose loss of coolant, flow loss, and steam generator tube rupture accidents, and its performance was compared with traditional fault diagnosis models to verify its effectiveness. The research shows that the constructed DFRBFNN model can accurately diagnose RCS faults under uncertain conditions.