Restricted　Boltzmann　machine　(RBM)　is　a　famous　model　for　feature　extraction　and　can　be　used　as　an　initializer　for　neural　networks.　When　applying　the　classic　RBM　to　multidimensional　data　such　as　2D/3D　tensors,　one　needs　to　vectorize　such　as　high-order　data.　Vectorizing　will　result　in　dimensional　disaster　and　valuable　spatial　information　loss.　As　RBM　is　a　model　with　fully　connected　layers,　it　requires　a　large　amount　of　memory.　Therefore,　it　is　difficult　to　use　RBM　with　high-order　data　on　low-end　devices.　In　this　article,　to　utilize　classic　RBM　on　tensorial　data　directly,　we　propose　a　new　tensorial　RBM　model　parameterized　by　the　tensor　train　format　(TTRBM).　In　this　model,　both　visible　and　hidden　variables　are　in　tensorial　form,　which　are　connected　by　a　parameter　matrix　in　tensor　train　format.　The　biggest　advantage　of　the　proposed　model　is　that　TTRBM　can　obtain　comparable　performance　compared　with　the　classic　RBM　with　much　fewer　model　parameters　and　faster　training　process.　To　demonstrate　the　advantages　of　TTRBM,　we　conduct　three　real-world　applications,　face　reconstruction,　handwritten　digit　recognition,　and　image　super-resolution　in　the　experiments.