To address the issue of unreliable neighborhood information due to the presence of anomalies， an efficient unsupervised graph anomaly detection method was proposed. In this method， a neighborhood enhancement strategy was utilized to construct multi-type neighborhood sets for central nodes， thereby capturing high-quality node representations and obtaining accurate self-neighborhood similarity. Firstly， an information extraction module based on dynamic neighborhood enhancement was optimized to select the optimal neighborhood strategy adaptively， thereby overcoming the feature homogeneity limitation of traditional fixed neighborhood selection methods during information extraction. Secondly， in order to reduce the interference of redundant information of the nodes during node feature fusion， an anonymous message passing scheme was proposed. In this scheme， features of the nodes were isolated， and neighborhood information was focused on solely， thereby enhancing the quality of message aggregation. Finally， an adaptive weighted anomaly scoring module was designed， using the distance between nodes as an evaluation scale to obtain node anomaly scores， thereby refining the anomaly detection results. Experimental results on five datasets demonstrate that the proposed method outperforms mainstream method CoLA （Anomaly detection on attributed networks via Contrastive self-supervised Learning） in detecting anomalies in complex graph structure， achieving an at least 8.0% AUPRC （Area Under the Precision-Recall Curve） improvement in identifying anomalous samples.