[Objective] Curtain grouting is a commonly used method for anti-seepage reinforcement in pumped storage projects, and injection rate is an important monitoring parameter that directly affects grouting quality. The fluctuation section of injection rate is a critical stage in the grouting process. At present, research on the analysis of injection rate monitoring data is scarce, scientifically sound criteria for reasonable injection rate are lacking, and it is difficult to guide the adjustment of grouting pressure. This paper proposes a method for analysing and calculating the injection-rate interval of the fluctuation section, providing scientifically sound intervals for different grouting scenarios. [Methods] By analysing the temporal evolution of injection rate during curtain grouting, the rate-time curve was divided into three stages—fluctuation, sharp-decline, and termination—and its patterns classified into four types: normal, sharp-decline, low-level injection, and non-convergent. Normal-pattern sections were selected as standard grouting segments, and two key parameters of the fluctuation section—unit-average injection rate and average slurry density—were calculated to indirectly represent the average geological conditions of the treated strata. Owing to the large scale of the grouting area and inherent geological variability of the treated strata, the unit-average injection rate exhibited high dispersion. Therefore, based on the intrinsic correlation between resource allocation decisions and geological information, the concept of “grouting similarity” and a dynamic geological-zoning approach were proposed. Correlation analysis between grouting parameters and unit-average injection rate selected GIN value, average slurry density, and hole sequence as energy, fracture, and sequence indices, respectively. Clustering was applied to standard grouting segments based on these indices to reduce dispersion. After de-noising the target parameters within each category using DBSCAN, based on the 3σ control principle in risk management, if the data followed a normal distribution, points falling outside the ±3σ range could, under the principle of controlling type Ⅰ and type Ⅱ errors, be identified as extreme outliers. The Shapiro-Wilk method was used to test the normality of each cluster, and for those that passed, the ±3σ interval was calculated as the injection rate interval for the fluctuation section. Based on the degree to which each segment's parameters deviated from the interval center, the specific interval of the fluctuation-section unit-average injection rate of completed segments was determined, and a preliminary post-grouting geological assessment was provided, thereby achieving data-driven quality management of curtain grouting. [Results] This method was applied to the curtain-grouting project of Wuyue Pumped-Storage Power Station for clustering analysis of completed grouting sections. Results showed that parameter distributions within most clusters satisfy the normality assumption. The calculated fluctuation-section injection rate intervals for each grouting-similarity pattern conformed to the similarity hypothesis and provided an effective control tool for injection rate process control and management. Based on these intervals, preliminary relative geological assessments precisely identified outlier sections with significant deviations from a large number of grouting segments and the proportion of outliers met quality-risk-management standards. [Conclusions] In summary, the proposed method for determining the injection rate interval of the fluctuation section is logically rigorous and reliable, significantly improving the scientific management of curtain-grouting construction in large-scale pumped-storage projects.