Mapping large-scale spatial patterns of grassland community properties in the Inner Mongolia Autonomous Region of China and learning how they are affected by environmental factors are vital to understand grassland changes in response to climate change and human activity. We collected data on six grassland community properties across 198 sample plots in the Inner Mongolia Autonomous Region: height, coverage, aboveground biomass (AGB), belowground biomass (BGB), soil bulk density (SBD), and species number (SN). We then analyzed the relationship between these and a range of environmental factors, including elevation, mean annual temperature (MAT), mean annual precipitation (MAP), ≥ 10 C annual accumulated temperature, humidity index, and normalized difference vegetation index (NDVI), using correlation and regression analysis. On the basis of the regression equation, we undertook a multifactor model using ArcGIS, in which different weights were assigned to each factor according to the degree of fitness between the estimated results and measured data. We then mapped the spatial distribution of grassland community properties in Inner Mongolia. We found a significant correlation between all of the grassland community properties and environmental factors measured (P < 0.01). In terms of spatial patterns, SN, height, coverage, AGB, and BGB were positively correlated with the transition from desert grassland to meadow grassland. The community properties model provided good results, with average accuracies of 53.05–90.21% and R² values of 0.40–0.68 (P < 0.01) across the six grassland community properties. The multifactor comprehensive model provides significant correlation between the predicted results and measured data. Therefore, this could be used as a basis for future studies on Inner Mongolia grasslands and to understand temporal and spatial changes of grassland in response to human activity and climate change.