For the issue of existing models’ inability to accurately identify intricate and diverse patterns of gang fraud， a new practical credit card fraud detection model based on complex transaction graph was proposed. Firstly， the association transaction graph was constructed based on the original transaction information of the users， then the graph Transformer neural network module was employed to mine the gang fraud characteristics directly from the transaction network without cumbersome feature engineering. Finally， the high-precision detection of fraud transactions was realized by jointly optimizing the topological features and sequential transaction features by the fraud detection network. The credit card anti-fraud experiment results showed that the proposed model outperformed seven benchmark models in all evaluation indexes. The Average-Precision （AP） improved by 20% and the Area Under the ROC Curve （AUC） increased by an average of 2.7% over the best benchmark Graph Attention Network （GAT） model in transaction fraud detection tasks. These results indicate that the proposed model is effective in the detection of credit card fraud transactions.