An anomaly detection method and device, a terminal and a storage medium are disclosed. The method may include: generating at least one clustering set of objects based on configuration data and performance indicator data of the objects; determining an algorithm configuration parameter corresponding to each clustering set based on a preset anomaly detection algorithm and the performance indicator data corresponding to the objects in the clustering set; and determining, based on the algorithm configuration parameter, abnormal performance indicator data of the objects in the corresponding clustering set, so as to determine abnormal objects based on the abnormal performance indicator data.