Speech　enhancement　is　a　challenge　that　has　not　been　well　solved　for　a　long　time.　The　current　research　attempts　to　improve　the　performance　of　speech　enhancement　by　microphone　array,　where　the　minimum　variance　and　distortionless　response　(MVDR)　beamformer　shows　a　good　performance　and　has　been　widely　studied　and　applied.　In　this　paper,　the　convolutional　neural　network　(CNN)　is　used　to　estimate　the　time-frequency　(T-F)　masking　of　target　speech　and　noise,　which　is　intended　to　obtain　covariance　matrix　in　the　MVDR.　On　the　other　hand,　the　steering　vector　is　estimated　through　main　feature　pattern　of　covariance　matrix,　instead　of　estimating　the　direction　of　arrival　angle　or　time　difference　of　arrival.　In　the　network　training,　the　training　goal　is　to　minimize　the　mean　square　error　(MSE)　between　the　enhanced　speech　and　real　speech,　and　the　loss　function　is　constructed　with　the　MSE.　Finally,　a　microphone　array　speech　simulator　is　used　to　generate　multi-channel　speech　and　the　related　simulation　experiment　is　carried　out.　The　experimental　results　show　that　the　CNN-based　MVDR　Beamforming　method　really　improved　the　performance　of　general　MVDR-based　beamformer.　©　2021　IEEE.