Graph-Level Anomaly Detection (GLAD) endeavors to pinpoint a small subset of anomalous graphs that deviate from the normal data distribution within a given set of graph data. Existing GLAD methods typically rely on Graph Neural Networks (GNNs) to extract graph-level representations, which are then used for the detection task. However, the inherent limited receptive field of GNNs may exclude crucial anomalous information embedded within the graph. Moreover, the inadequate modeling of cross-graph relationships limits the exploration of connections between different graphs, thus restricting the model's ability to uncover inter-graph anomalous patterns. In this paper, we propose a novel approach called Dual-View Graph-of-Graph Representation Learning Network for unsupervised GLAD, which takes into account both intra-graph and inter-graph perspectives. Firstly, to enhance the capability of mining intra-graph information, we introduce a Graph Transformer that enhances the receptive field of the GNNs by considering both attribute and structural information. This augmentation enables a comprehensive exploration of the information encoded within the graph. Secondly, to explicitly capture the cross-graph dependencies, we devise a Graph-of-Graph-based dual-view representation learning network to explicitly capture cross-graph interdependencies. Attribute and structure-based graph-of-graph representations are induced, facilitating a comprehensive understanding of the relationships between graphs. Finally, we utilize anomaly scores from different perspectives to quantify the extent of anomalies present in each graph. This multi-perspective evaluation provides a more comprehensive assessment of anomalies within the graph data. Extensive experiments conducted on multiple benchmark datasets demonstrate the effectiveness of our proposed method in detecting anomalies within graph data.