Abstract: As critical infrastructure nodes in waterway transportation networks, inland ship locks play a pivotal role in ensuring the efficiency and safety of shipping logistics systems. Therefore, it is of great practical significance to assess and enhance the emergency management capability of inland river lock operations. However, despite its importance, there are few studies focusing on the evaluation of the emergency management capability of inland ship locks. To address this gap, this study develops an evaluation framework integrating improved combination weighting and a cloud model, providing a systematic and quantitative approach for assessing the emergency management capability of inland ship lock operations. First, a comprehensive evaluation index system was established, grounded in the PPRR theory (emergency prevention, preparedness, response, and recovery) and refined through expert opinions. This index system comprises four first-level indicators and 30 second-level indicators, covering the complete emergency management lifecycle from risk prevention to post-incident recovery. Second, an improved G1 method and a modified CRITIC method were employed to determine subjective and objective weights respectively. The game theory method was then applied to optimize the combination weights, ensuring rational and scientifically sound weight allocation. Third, the cloud model was employed to effectively transform qualitative concepts into quantitative data, addressing the inherent randomness and fuzziness in emergency management capability evaluations while enabling intuitive visualization of results through cloud maps. Based on the weight calculation results, the ranking of first-level indicators by relative importance is emergency response (B₃, 0.336 0), emergency prevention (B₁, 0.320 9), emergency preparedness (B₂, 0.234 3), and emergency recovery (B₄, 0.108 9). Among these, B3 carries the highest weight, highlighting the critical importance of real-time handling capabilities in ship lock operations. At the second-level indicator level, the top three indicators by importance are the emergency management system and organizational structure (C₁₂, 0.042 6), experience summarization and improvement (C₃₀, 0.037 6), and lock gate operation monitoring (C₁, 0.033 4). It is worth noting that significant differences exist between subjective and objective weights for certain indicators due to the distinct logical foundations of different weighting methods. Through the game theory–based optimization model that combines both subjective and objective weights, the limitations inherent in using either approach alone can be effectively mitigated, thereby enhancing both the scientific validity and accuracy of weight allocation. A ship lock in Jiangsu Province was selected as a case for empirical analysis, and its operational emergency management capability from 2020 to 2023 was evaluated. The comprehensive cloud digital characteristics of its emergency management capability over the years are (80.767, 2.974, 1.407), (84.330, 2.643, 1.162), (87.867, 2.741, 1.215), and (88.975, 2.938, 1.115), respectively. Overall, the emergency management capability of this ship lock has performed well and has gradually improved since 2020. The emergency management level progressed steadily in 2020 and 2021, and improved significantly after 2022. The evaluation results obtained from the cloud model are broadly consistent with the actual situation of the ship lock, confirming the effectiveness and scientific nature of this assessment method. Furthermore, the results for the first-level indicators show rightward shifts, reflecting the positive development of the lock’s emergency management system under the PPRR framework. Notably, B₃ and B₄ capabilities improved significantly, whereas B₁ and B₂ exhibited slower growth, indicating room for further improvement in these two dimensions. A detailed examination of the second-level indicators revealed specific weaknesses in power system monitoring (C₃), meteorological monitoring (C₇), emergency material storage (C₁₃), and dissemination of emergency knowledge (C₁₅), suggesting areas for targeted improvement by management. Finally, practical recommendations are provided accordingly. This research makes important theoretical contributions by advancing the application of combination weighting methods in infrastructure management and by demonstrating the effectiveness of the cloud model for complex system evaluations. The study’s findings also offer substantial practical value for waterway management authorities. The proposed evaluation framework serves as a standardized tool for benchmarking emergency management performance across different locks and for tracking improvements over time. This study provides both a theoretical basis and practical guidance for management departments to optimize emergency management strategies and enhance management efficiency.