Abstract: Graph anomaly detection plays a crucial role in identifying abnormal patterns within complex data structures, and finds applications in various domains such as malicious molecule identification, financial fraud detection, and social network analysis. While existing research in graph anomaly detection predominantly focuses on node-level anomaly detection, there is a scarcity of methods specifically designed for graph-level anomaly detection. Moreover, these methods often fail to adequately explore anomalous graph data, are sensitive to anomaly labels, struggle to capture features of anomalous samples effectively, exhibit poor model generalization, and suffer from performance reversal issues. There is a need for improvement in anomaly detection capabilities. we propose an anomaly-aware variational graph autoencoder based graph-level anomaly detection algorithm (VGAE-D), which utilizes an anomaly-aware variational graph autoencoder to simultaneously extract features from normal and anomalous graph data. This algorithm distinguishes the encoding information of normal and anomalous graphs in the encoding space, further exploring the graph encoding information to compute anomaly scores. Experimental evaluations on eight publicly available datasets from diverse domains demonstrate the effectiveness of the proposed graph-level anomaly detection algorithm. The results indicate that VGAE-D can efficiently identify anomalous graphs in different datasets, outperforming mainstream graph-level anomaly detection methods. Additionally, the algorithm exhibits a capability for learning from few anomalous samples, mitigating the issue of performance inversion to a large extent.