Abstract: Gully erosion is a severe form of soil erosion on sloping farmland in the black soil region of Northeast China, posing a major threat to food security and ecological security. This study conducted field investigations of gullies in the Xingjiadian small watershed of Jilin Province and performed statistical analyses of their morphological and topographic parameters. Spatial autocorrelation analysis and the geographical detector were further applied to identify the spatial distribution characteristics and dominant factors of gully line density, area density, and kernel density at the grid and sub-watershed scales. The main conclusions are as follows: (1) The gullies within the small watershed vary considerably in scale, but their cross-sectional shapes, geometric forms, and topographic development conditions are relatively similar. (2) The spatial distribution of gullies shows a clear positive spatial autocorrelation. Local clusters and hotspot areas are mainly concentrated in the northwestern and southern parts of the watershed, and kernel density provides strong spatial explanatory power for gully distribution patterns. The grid scale is suitable for revealing spatial heterogeneity and local aggregation characteristics of gullies, whereas the sub-watershed scale is more appropriate for analyzing the overall regional erosion pattern. (3) Land use, distance to residence, distance to cropland, ridge direction, elevation, slope, surface relief, surface roughness, and NDWI are identified as the dominant factors influencing gully development. Among these, the combination of “elevation + land use” achieves the highest explanatory power, and combinations involving most human-activity-related factors generally exhibit stronger explanatory effects. The results provide quantitative evidence for understanding the spatial distribution patterns and development mechanisms of gullies in the black soil region of Northeast China and offer references for spatial planning and integrated watershed management in future risk assessment and prediction.