Soil moisture data play a key role in the study of climate change and agricultural drought monitoring, agricultural weather forecast and service. In order to effectively eliminate the abnormal data in observations, this paper puts forward a set of quality control (QC) methods which could be applied to the data of automatic soil moisture observation station (ASMOS) in China. First, based on the data of ASMOS 2014 in China, the abnormal data are divided into four categories according to their characters. Secondly, under the consideration of three aspects: threshold value check, internal consistency check, time consistency check, the QC methods are designed, which include abnormal extreme check, abnormal increase check, abnormal decrease check and abnormal constant check. Finally, the QC methods are verified by using the data of ASMOS and soil volumetric water content products of CMA Land Data Assimilation System (CLDASV2.0) in China in 2014-2015. The results show that: (1) the four kinds QC methods can effectively identify the four types of abnormal data. (2) The results from the four kinds QC methods are in good agreement in temporal continuity and spatial distribution. (3) The QC methods can effectively reduce the root mean square error (RMSE) between observation and the CLDAS data. At present, the methods have been applied to the Meteorological Data Processing Service System.