Graph anomaly detection has achieved significant progress in recent years. However, traditional graph models are limited to binary relations and local structures, making it difficult to capture higher order interactions among nodes. To address this limitation, we propose a novel anomaly detection method based on dynamic hypergraph neural network (DHGAD). The method first integrates structural and attribute information to construct an initial graph that reflects dual similarity. To capture higher order and tightly knit node dependencies, a hypergraph is constructed based on representative motif structures extracted from the initial graph subsequently. A dynamic hypergraph update mechanism is introduced to refine the structure using reconstruction loss iteratively, enabling nodes to capture richer structural semantics. Finally, anomaly detection is enhanced by shifting from “node-neighbor” to “node-neighborhood” consistency, improving robustness by analyzing behavioral deviations within broader local contexts. Experiments on multiple datasets demonstrate the effectiveness and superiority of the proposed approach.