CEIRA, a computer program developed by Suzhou Nuclear Power Research Institute Co., Ltd. (SNPI), is designed for assessing the potential consequences of nuclear power plant accidents. It is based on and improved upon the Regulatory Guide 1.145 and the program PAVAN of NRC (U.S. Nuclear Regulatory Commission). Widely utilized in safety analysis and environmental impact assessment of nuclear facilities, the program calculates public radiation doses at the boundary of exclusion area (EA) and the planning restricted area (PRA) by inputting hourly meteorological data, accident source terms, and other parameters to assess the radiological consequences of potential nuclear accidents. When using CEIRA, the influence of input parameters on the results is inevitable, particularly when dealing with complex atmospheric dispersion processes and diverse accident scenarios. To understand the extent of this influence, this study conducted a systematic uncertainty and sensitivity analysis of CEIRA's input parameters. A specific design basis accident (DBA) scenario at a reactor was selected as a case study. Key input parameters, including height of the reactor building, vertical-plane cross-sectional area of the reactor building, atmospheric dispersion coefficients, dry deposition velocity, and breathing rates, were sampled using the Latin Hypercube Sampling (LHS) method. These samples were analyzed through correlation and regression techniques to evaluate the impact of parameter variations on target outputs such as relative air concentration (X/Q), effective dose and thyroid dose. The results indicate that variations in key parameters significantly influence the evaluated accident consequences. For instance, changes in atmospheric dispersion parameters notably affect spatial dose distribution, while dry deposition velocity had a more pronounced impact on ground contamination accumulation. The uncertainty analysis quantified each parameter’s contribution to the overall uncertainty of dose estimates, providing critical quantitative data. Sensitivity analysis further identified the ranking of parameter importance, offering valuable insights for improving the accuracy of evaluations and optimizing parameter settings in the program. This study provides scientific support for the optimization of the CEIRA program and its application in safety analysis and environmental impact assessment of nuclear facility. It also offers a solid data foundation for decision-making and emergency response planning under scenario of nuclear accident. Moreover, the analytical methodology demonstrated here can be extended to other software or similar tools in safety analysis and environmental impact assessment, enhancing the credibility and scientific rigor of potential accident consequence assessment at nuclear facilities.