Aiming at the problem that the existing vehicle insurance fraud detection methods ignore complex correlation in the data， a vehicle insurance fraud detection method based on improved graph attention network was proposed. This method enhances the ability to capture complex correlation in the data through collaborative design of dynamic attention mechanism and serialized global modeling. Firstly， each case of vehicle insurance fraud was abstracted as a node of graph structure. Secondly， the similarity between multiple attributes such as time， age， and amount of the nodes was calculated by K-Nearest Neighbor （KNN） algorithm， so as to construct the complex correlation between the cases. Thirdly， the graph data of the cases was input into GATv2（dynamic Graph ATtention network）， and local features of the adjacent nodes were aggregated by allocating node weights dynamically， thereby obtaining new feature representation of each case node. Fourthly， Transformer was introduced to serialize the graph structure output of GATv2. Finally the fusion module was used to perform nonlinear integration expression on the final features， so as to obtain the classification results of the case nodes. Experimental results show that compared with the baseline methods， the proposed method has the accuracy on the two datasets improved by at least 1.11 and 1.34 percentage points， respectively， and the False Positive Rate （FPR） of as low as 0.035% on the insurance company dataset， which provides a new technical solution for improving the accuracy and efficiency of vehicle insurance fraud detection.