A method of heuristic reduction support vector data description(HR-SVDD) is proposed for speeding up the support vector data description(SVDD) one-class classification method. The HR-SVDD first builds a reduced training set by selecting a portion of samples from training set in heuristic, and then completes the quadratic programming using the reduced training set rather than the original training set. The efficiency of proposed method is demonstrated by discussing characteristic of Gaussian kernel SVDD with different width parameters. For demonstration, experiments on artificial and real-world datasets are conducted, and the results show that the classification accuracy of HR-SVDD is nearly identical to that of conventional SVDD, but with faster running speed and less memory usage.