Spatial aggregation features of 2015 county-level grain yields of Huang-Huai-Hai Region have been analyzed systematically based on cumulative distribution function and spatial autocorrelation analysis method, and impact of 18 factors on grain yields of different categories of counties in Huang-Huai-Hai Region and their interaction have been analyzed by use of geographical detector. The results indicate that low county-level grain yields in Huang-Huai-Hai Region tend to aggregate and high county-level grain yields tend to scatter, showing significant homogeneous aggregation in space. The areas of significant high yields are mainly distributed in southeast Henan Province, north Anhui Province and north Jiangsu Province and areas of significantly low yields are mainly in Beijing-Tianjin-Hebei Region and coastal counties of Shandong Province. In consideration of spatial constraints and distribution difference of grain yields, Huang-Huai-Hai Region can be classified into 4 areas: high grain yield area, mid-high grain yield area, low-middle grain yield area and low grain yield area. The impacts of 18 factors on county-level grain yields of Huang-Huai-Hai Region vary and mainly manifest dual-factor enhancement type and nonlinear enhancement type. The leading factors of high yield area are added value of primary industry, consumption of fertilizers (total mass percent of nutrients) and total agricultural mechanical power, belonging to social economy and factor-input acting type. The leading factors of mid-high yield area are cultivated land area, regional registered population, added value of primary industry, gross agricultural mechanical power, showing as the combined acting type. The leading factors of low-middle yield area are cultivated land area and consumption of fertilizers (total mass percent of nutrients), showing as geographical environment and factor-input acting type. The leading factors of low yield area are vegetation index, cultivated land area, added value of primary industry, consumption of fertilizers (total mass percentage of nutrients) and total agricultural mechanical power, showing as a combined acting type. The targeted grain production and income increase strategy shall be formulated in the future based on actual conditions of different areas.