Restricted　Boltzmann　Machine　(RBM)　is　a　particular　type　of　random　neural　network　models　modeling　vector　data　based　on　the　assumption　of　Bernoulli　distribution.　For　multidimensional　and　non-binary　data,　it　is　necessary　to　vectorize　and　discretize　the　information　in　order　to　apply　the　conventional　RBM.　It　is　well-known　that　vectorization　would　destroy　internal　structure　of　data,　and　the　binary　units　will　limit　the　applying　performance　due　to　fickle　real　data.　To　address　these　issues,　this　paper　proposes　a　Matrix　variate　Gaussian　Restricted　Boltzmann　Machine　(MVGRBM)　model　for　matrix　data　whose　entries　follow　Gaussian　distributions.　Compared　with　some　other　RBM　algorithms,　MVGRBM　can　model　real　value　data　better　and　it　has　good　performance　in　image　classification.　To　prove　that　adding　Gaussian　parameters　could　model　input　data　well,　we　compared　the　reconstruction　performance　of　the　Gaussian　parameters　updating　and　fixed.　©　2017　IEEE.