With the continuous development of Internet technology and the continuous expansion of network scale, new network services emerge in an endless stream. In order to ensure the quality of user service, accurate and rapid classification of application traffic is the focus of current research. The traditional service identification method is based on protocol or specific service classification, which is suffered from low applicability. Combining traffic characteristics and machine learning methods, this paper proposes a traffic identification method based on the fusion of Generative Adversative Network (GAN) and Extreme Gradient Lift Boosting (XGBoost). Firstly, the traffic characteristics representing service resource requirements. Then GAN algorithm was improved to expand a few class samples to solve the problem of low model accuracy caused by the unbalanced distribution of data sets in the process of application identification. Finally, the random forest algorithm was used to select the feature, and the XGBoost algorithm was used to complete the model training. The results show that the accuracy of this method is 97.32%.