The clustering of the credit system construction environment of different provinces is studied. From social, economic, political, financial dimensions, the influential factors of the credit system construction environment are raised and 12 secondary indicators are established to measure the performance of social credit system construction. Based on the time series data of 31 provinces and cities in China from 2009 to 2013，the time weight of each year is determined with the Orness measures. Clustering based on K- means algorithm provides reference for classification and target management of social credit system construction.