Matrix-variate　Restricted　Boltzmann　Machine　(MVRBM),　a　variant　of　Restricted　Boltzmann　Machine,　has　demonstrated　excellent　capacity　of　modelling　matrix　variable.　However,　MVRBM　is　still　an　unsupervised　generative　model,　and　is　usually　used　to　feature　extraction　or　initialization　of　deep　neural　network.　When　MVRBM　is　used　to　classify,　additional　classifiers　must　be　added.　In　order　to　make　the　MVRBM　itself　be　supervised,　in　this　paper,　we　propose　improved　MVRBMs　for　classification,　which　can　be　used　to　classify　2D　data　directly　and　accurately.　To　this　end,　on　one　hand,　classification　constraint　is　added　to　MVRBM　to　get　Matrix-variate　Restricted　Boltzmann　Machine　Classification　Model　(ClassMVRBM).　On　the　other　hand,　fisher　discriminant　analysis　criterion　for　matrix-style　variable　is　proposed　and　applied　to　the　hidden　variable,　therefore,　the　extracted　feature　is　more　discriminative　so　as　to　enhance　the　classification　performance　of　ClassMVRBM.　We　call　the　novel　model　Matrix-variate　Restricted　Boltzmann　Machine　Classification　Model　with　Fisher　discriminant　analysis　(ClassMVRBM-MVFDA).　Experimental　results　on　some　publicly　available　databases　demonstrate　the　superiority　of　the　proposed　models.　Of　which,　the　image　classification　accuracy　of　ClassMVRBM　is　higher　than　conventional　unsupervised　RBM,　its　variants　and　supervised　Restricted　Boltzmann　Machine　Classification　Model　(ClassRBM)　for　vector　variable.　Especially,　the　image　classification　accuracy　of　the　proposed　ClassMVRBM-MVFDA　performs　better　than　supervised　ClassMVRBM　and　vectorial　RBM-FDA.