In the context of big data，data nodes in unstructured cloud data management systems need to process ever-expanding raw data，index data and intermediate data． “Data expansion”will significantly increase the time and energy consumption of cloud data management systems． In order to reduce the data transmission overhead caused by the frequent movement of data in unstructured data management system，an algorithm for identifying unstable data partitions was proposed． Firstly，for the unstructured data management system，the cloud model of data partition was conducted in the storage system by introducing the cloud model theory，and unstable data partitions were identified． Then the relevant algorithm was called to re-layout unstable data partitions． Experimental results show that the unstable data partition identification algorithm can effectively identify unstable data partitions and relayout them，and significantly reduce data transmission overhead．