In order to understand the characteristics of soil protozoa communities under different vegetations, soil samples were collected from 7 different vegetations in Dinghu Mountain, the community composition and abundance of soil Sarcondina were studied with the methods of qualitative and quantitative cultivation and the distribution pattern of soil Sarcondina in the small range was investigated based on the ubiquity model and biogeography model. In total 20 genera and 31 species of soil Sarcondina were found in the soil samples, and the Genus Centropyxis and Nebela were found to harbor the most species (both 3 species). Among all species, Amoeba sp., Centropyxis compressa, Difflugia globulosa, D. gramen, Lamtopyxis sp., Nebela dentistoma, Trinema enchelys and T. lineare were found in four sites. There were distinct differences in soil Sarcondina community composition under different vegetations, there most species (15 species) were observed under evergreen broad-leaved forest in the montane (Sample F) and the least species (9 species) under shrub and grass near to top of mountain (Sample G). There were various abundance distributions of soil Sarcondina communities under different vegetations, the highest abundance of soil Sarcondina was in Site F (29 200 ind./g) and the lowest was in Site G (3 510 ind./g). The Sarcodina community similarity index was in the level from moderate unsimilarity (0.25 - 0.5) to moderate similarity (0.5 - 0.75). Cluster analysis showed that the highest community similarity was found between the Sample A (evergreen broad leaved forest in valley) and Sample F (evergreen broad-leaved forest in montane), and community similarity between Sample B (evergreen broad-leaved forest in riparian) and Sample G (the shrub and grass near to top of the mountain) was higher, meanwhile the relatively lower community similarity between Sample D (coniferous and broad-leaved mixed forest) and other samples were noticed. Correlation analysis revealed that there was an extremely significant correlation (P<0.01) between soil water content and the abundance of the Sarcodina, and there was a significant correlation between soil NO₃^--N and the abundance of the Sarcodina (P<0.05). The multiple correlation analysis showed that there were correlations between the abundance of the soil Sarcodina and the combinations of soil physic-chemical factors, particularly with soil pH and water content. CCA analysis showed that different soil physical and chemical factors had different affects on different Sarcodina species. The results displayed the distribution of soil Sarcodina in Dinghu Mountain was consistent with Foissner “biogeography model”.