In　this　paper,　a　multi-channel　speech　enhancement　method　with　the　minimum　variance　distortionless　response　(MVDR)　beamforming　method　based　on　the　time-frequency　(T-F)　masking　is　proposed.　In　this　study,　First,　the　logarithmic　power　spectrum　(LPS)　features　of　multi-channel　signals　are　used　as　input　features　to　estimate　a　T-F　mask　of　the　reference　microphone　by　the　deep　neural　network　(DNN)　model.　Then,　the　estimated　mask　is　utilized　to　calculate　speech　covariance　matrix　that　is　used　to　estimate　a　steering　vector　for　constructing　the　MVDR　beamformer.　The　steering　vector　is　estimated　by　the　generalized　eigen-value　decomposition　(GEVD)　method.　Finally,　the　output　speech　of　the　beamformer　is　processed　by　the　DNN-based　IRM　model.　In　order　to　prove　the　effectiveness　of　the　proposed　method,　the　perceptual　evaluation　of　speech　quality　(PESQ)　and　the　segment　signal-to-noise　ratio　(SSNR)　are　employed.　The　experimental　results　show　that　the　proposed　method　effectively　increased　the　PESQ　and　SSNR.