Accurate traffic flow forecasting can be applied to traffic control and management to improve operating efficiency of the lagre-scale road network. Aiming at how to fit the spatio-temporal distribution of traffic flow with high precision and accurate forecasting, an algorithm of lagre-scale road network traffic flow forecasting based on Wasserstein generative adversarial network with gradient penalty is proposed. According to the characteristics of traffic flow data for large-scale road network, the proposed algorithm used residual U-Net as a generator to increase the network depth for improving the ability of model to abstract the characteristics of temporal correlation and long-distance spatial correlation.The proposed algorithm can solve the problem that the discriminant deep learning models can only minimize the whole error of the road network while ignoring the error minimization of each observation point, then meet the demand of real traffic scenes better. Experimental results show that the proposed algorithm can learns the coupling characteristics of multi-factor inside the traffic flow data in lagre-scale road network effectively and improve the prediction accuracy.