Abstract: Safety-focused process system analysis is an important approach to ensure the safety of radioactive waste disposal facilities throughout their lifecycle, including siting, construction, operation, and closure. In recent years, the uncertainty in the analysis process receives increasing attention, and uncertainty analysis and management become integral parts of safety-focused process system analysis and safety evaluation. International documents such as the Safety-Focused Process System Analysis and Safety Evaluation for Radioactive Waste Disposal by IAEA and domestic regulations like the Safety Regulations for Near-Surface Disposal of Low and Intermediate-Level Radioactive Solid Waste emphasize the importance of uncertainty analysis in these processes. Properly addressing uncertainty in safety-focused process system analysis and safety evaluation is crucial for ensuring the credibility of the evaluation results. Therefore, conducting uncertainty analysis on case studies for long-term safety evaluations is particularly important. External factors, such as environmental changes and human behavior, which cannot be controlled in advance, significantly impact the safety of various nuclear facilities, including radioactive waste disposal facilities, and thus require uncertainty assessment. Many countries and international organizations in the nuclear industry conducted related research, focusing on probability, risk guidance, and uncertainty analysis. They developed some general evaluation methods, such as Monte Carlo simulation and Latin hypercube sampling, for uncertainty analysis according to specific circumstances. In the uncertainty analysis of radioactive waste disposal repositories, the computational process of parameter uncertainty analysis is more complex compared to the common application scenarios of Monte Carlo simulation. It involves a large number of random input parameters and complex models, which necessitates smaller sample sizes to reduce computation time and improve sampling efficiency. In this case, the sampling process with small sample sizes and multiple random parameters will inevitably face a challenge: Whether the uniformity and correlation between the dimensions of the sampled samples meet the computational requirements. To address the issue of uniformity in the sampling process, adopting Latin hypercube sampling (LHS) coupled with Monte Carlo simulation is a common choice. In this study, LHS was improved by applying the Cholesky decomposition method in the parameter uncertainty analysis of safety-focused process system analysis for radioactive waste disposal repositories. The improved LHS method through this approach can use smaller samples to meet the correlation requirements of the computation, thereby enhancing sampling efficiency. In the scenario of this study, the improved sampling method only requires one-tenth of the sample size needed in the original method.