Predicting of airfoil aerodynamic performance is a key part of aircraft design optimization, but the traditional methods (such as wind tunnel test and CFD simulation) have the problems of high cost and low efficiency, and the existing data-driven models face the challenges of insufficient accuracy and strong data dependence in multi-objective prediction. Therefore, this study proposes a deep learning model, Deeptrans, based on the fusion of improved Transformer and generative Adversarial network (GAN), which aims to predict the multi-parameter aerodynamic performance of airfoil efficiently. By constructing a large-scale data set and designing a model structure that integrates a Transformer coding-decoding framework and confrontation training, synchronous and high-precision prediction of aerodynamic parameters is realized. Experiments show that the MSE loss of Deeptrans on the verification set is reduced to 5.6*10-6, and the single-sample prediction time is only 0.0056 seconds, which is nearly 700 times more efficient than the traditional CFD method. Horizontal comparison shows that the prediction accuracy is significantly better than the original Transformer, GAN, and VAE models. This study provides an efficient data-driven solution for airfoil aerodynamic performance prediction and a new idea for deep learning modeling complex flow problems.