Speech　enhancement　in　the　noisy　and　reverberant　environment　remains　a　challenging　task.　Acoustic　beamforming　algorithm　with　minimum　variance　distortionless　response　(MVDR)　has　shown　to　be　effective　for　this　case.　The　crucial　issue　in　MVDR-based　speech　enhancement　is　to　get　accurate　estimates　of　the　speech　and　noise　spatial　covariance　matrices　(SCMs).　On　this　way,　time-frequency　mask-based　method　which　is　a　reliable　method　to　estimate　the　SCMs　can　improve　the　performance　of　the　MVDR　beamformer　in　speech　enhancement.　In　this　paper,　an　optimal　ratio　mask-based　method　used　for　MVDR　beamforming　is　proposed.　Specifically,　the　convolutional　neural　networks　(CNNs)　is　used　in　the　proposed　method,　which　operates　on　the　magnitude　and　phase　components　of　the　short-time　Fourier　transform　(STFT)　of　microphones　to　estimate　the　optimal　ratio　masks,　and　these　masks　are　used　to　get　the　SCMs　for　constructing　MVDR　beamformer.　Experiments　are　conducted　by　using　simulated　data.　The　results　show　that　the　proposed　method　is　more　robust　than　the　reference　methods　against　the　terrible　acoustic　conditions.