In order to reduce the regression test set and improve the efficiency of regression test in the Continuous Integration （CI） environment， a regression test suite selection method for the CI environment was proposed. First， the commits were prioritized based on the historical failure rate and execution rate of each test suite related to each commit. Then， the machine learning method was used to predict the failure rates of the test suites involved in each commit， and the test suite with the higher failure rate were selected. In this method， the commit prioritization technology and the test suite selection technology were combined to ensure the increase of the failure detection rate and the reduction of the test cost. Experimental results on Google’s open-source dataset show that compared to the methods with the same commit prioritization method and test suite selection method， the proposed method has the highest improvement in the Average Percentage of Faults Detected per cost （APFDc） by 1% to 27%； At the same cost of test time， the TestRecall of this method increases by 33.33 to 38.16 percentage points， the ChangeRecall increases by 15.67 to 24.52 percentage points， and the test suite SelectionRate decreases by about 6 percentage points.