In order to solve the problem that traditional Support Vector Data Description (SVDD) is quite sensitive to penalty parameters, a new detection method, called Distribution Entropy Penalized SVDD (DEP-SVDD), was proposed. First, the normal samples were taken as the global distribution of the data, and the distance measure between each sample point and the normal sample distribution center was defined in the Gaussian kernel space. Then, a probability was defined for every data point, which was able to estimate the possibility of the point belonging to normal sample or abnormal one. Finally, the probability was used to construct the punishment degree based on distribution entropy to punish the corresponding samples. On 9 real-world datasets, the proposed method was compared with the algorithms of SVDD, Density Weighted SVDD (DW-SVDD), Position regularized SVDD (P-SVDD), K-Nearest Neighbor ( KNN) and isolation Forest (iForest). The results show that DEP-SVDD achieves the highest classification precision on 6 datasets, which proves that DEP-SVDD has better performance advantages in anomaly detection than many anomaly detection methods.