The objective of multidimensional graph fraud detection is to identify fraudulent entities within a graph. Graph Neural Network (GNN) models leverage graph structures to propagate messages from neighbors to target nodes, thereby obtaining precise representations of the target nodes. However, anomaly detection methods based on GNNs face challenges, such as structural inconsistency and over-smoothing, which reduce the suspicion scores of fraudulent nodes and hinder the effectiveness of anomaly detection models. To address these issues, we propose an end-to-end anomaly node detection model named Feature Decomposition Graph Neural Network (FD-GNN). In FD-GNN, a feature amplification module is first employed to enhance the differences between node representations. Then, a feature decomposition module is used to distinguish the natural attributes of nodes from their neighborhood attributes, with constraints applied to both types of attributes. Finally, a representation aggregation module utilizes differential aggregation operations to further distinguish normal nodes from anomaly ones. Experiments conducted on two real-world datasets, Amazon and YelpChi, demonstrate that FD-GNN achieves Area Under Curve (AUC) scores of 94.45% and 91.62%, respectively, outperforming existing multidimensional graph anomaly detection models.