Anomaly subgraph detection is a vital task in various real applications. However, with the advancement of AI technology, it faces new challenges: 1) Anomaly features are often deeply hidden within large datasets, and 2) Anomaly detection approaches are required to unveil the mechanisms behind anomaly generation. Our study focuses on detecting hidden anomaly subgraphs within big data and offering improved explanations for the root cause of anomalies by integrating multi-domain datasets.