With the intensification of global climate change, exploring the impact of environmental factors on tree density can provide technical support for sustainable forest management. In this paper, the random forest parameters nTree and mtry were optimized using a particle swarm optimization algorithm. The density, average temperature, soil thickness, forest water consumption, slope, slope direction, slope position, soil type, and diameter at breast height (DBH) of the dominant tree species in Inner Mongolia were fitted using random forest regression with a satisfactory fitting effect (R2 > 0.60). The results show that the average temperature, soil thickness, and forest water consumption were the main factors restricting tree density, and the influence of each factor changed depending on the stage of tree growth. Based on 2018 forest resource data of the Inner Mongolia Autonomous Region, four diameter class models were used to calculate tree density, and Kriging interpolation was used to form a density distribution grid map of the main tree species according to diameter class toward providing a theoretical basis and data support for afforestation and forest management strategies that are justified according to the available environmental resources.
