Recently,　Restricted　Boltzmann　Machine　(RBM)　has　demonstrated　excellent　capacity　of　modelling　vector　variable.　A　variant　of　RBM,　Matrix-variate　Restricted　Boltzmann　Machine　(MVRBM),　extends　the　ability　of　RBM　and　is　able　to　model　matrix-variate　data　directly　without　vectorized　process.　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　are　necessary.　This　paper　proposes　a　Matrix-variate　Restricted　Boltzmann　Machine　Classification　Model　(ClassMVRBM)　to　classify　2D　data　directly.　In　the　novel　ClassMVRBM,　classification　constraint　is　introduced　to　MVRBM.　On　one　hand,　the　features　extracted　by　MVRBM　are　more　discriminative,　on　the　other　hand,　the　proposed　model　can　be　directly　used　to　classify.　Experiments　on　some　publicly　available　databases　demonstrate　that　the　classification　performance　of　ClassMVRBM　has　been　largely　improved,　resulting　in　higher　image　classification　accuracy　than　conventional　unsupervised　RBM,　its　variants　and　Restricted　Boltzmann　Machine　Classification　Model　(ClassRBM).　©　ICST　Institute　for　Computer　Sciences,　Social　Informatics　and　Telecommunications　Engineering　2019.