In the complex application environment of the global satellite navigation system （GNSS）， where various kinds of suppressive interference and spoofing randomly exist， the traditional interference suppressing method that first estimates the interference parameters and then canceles interference signal， will be designed difficultly and has low generality， because the special parameter estimators and the interference reducing methods are needed for various types of interference. Therefore， an interference suppression scheme based on deep residual neural networks （DRNNs） is proposed in this paper. First， the corresponding DRNN is built and trained for each typical GNSS interference. It can directly extract the target satellite signal from the interfered signal. Second， according to the interference classification and recognition result， the corresponding DRNN is selected. The time-frequency two dimensional （2D） signals obtained by short-time Fourier transform over the received one-dimensional signal are then entered into the chosen DRNN. The output is the 2D time-frequency spectrum of the useful signal， where the impact of the interference has been suppressed. In our scheme， the same procedure is applied for different kinds of suppressive interference and spoofing. It is not required to design the special designs about the parameter estimation and the interference reduction for various interferences. Experimental results show that the proposed scheme can effectively suppress various GNSS interference， compared with the traditional scheme. It demonstrates a certain of commonality.