With the increasing complexity of multidimensional data features， the existing anomaly detection methods have limitations in capturing feature distribution. At the same time， traditional clustering and statistical methods encounter greater challenges in parameter selection， which limit the improvement of detection performance together. To address this issue， an anomaly detection method based on cumulative probability fluctuation and automated clustering was proposed. Firstly， cumulative probability fluctuation of the features was calculated to represent the Gaussian mixture distribution of the features， and the features were compression transformed according to the cumulative probability fluctuation values. Secondly， deep reinforcement learning was employed to search optimal clustering parameters in Density-Based Spatial Clustering of Applications with Noise （DBSCAN）， and the compression transformed dataset was clustered. Finally， the clustering results of the data were combined with the cumulative probability fluctuation values of the data features to determine data point anomalies. Experimental results show that the average precision， recall， F1-score， and Area Under ROC （Receiver Operating Characteristic） Curve （AUC） of the proposed method on six experimental datasets are 36.39%， 2.73%， 14.90%， and 4.84% higher than those of the best performing method among the comparison methods. It can be seen that the proposed method improves the comprehensive performance of anomaly detection for data with multi-dimensional complex features effectively without selecting parameters manually.
