In　recent　years,　deep　learning　caves　out　a　research　wave　in　machine　learning.　With　outstanding　performance,　more　and　more　applications　of　deep　learning　in　pattern　recognition,　image　recognition,　speech　recognition,　and　video　processing　have　been　developed.　Restricted　Boltzmann　machine　(RBM)　plays　an　important　role　in　current　deep　learning　techniques,　as　most　of　existing　deep　networks　are　based　on　or　related　to　it.　For　regular　RBM,　the　relationships　between　visible　units　and　hidden　units　are　restricted　to　be　constants.　This　restriction　will　certainly　downgrade　the　representation　capability　of　the　RBM.　To　avoid　this　flaw　and　enhance　deep　learning　capability,　the　fuzzy　restricted　Boltzmann　machine　(FRBM)　and　its　learning　algorithm　are　proposed　in　this　paper,　in　which　the　parameters　governing　the　model　are　replaced　by　fuzzy　numbers.　This　way,　the　original　RBM　becomes　a　special　case　in　the　FRBM,　when　there　is　no　fuzziness　in　the　FRBM　model.　In　the　process　of　learning　FRBM,　the　fuzzy　free　energy　function　is　defuzzified　before　the　probability　is　defined.　The　experimental　results　based　on　bar-and-stripe　benchmark　inpainting　and　MNIST　handwritten　digits　classification　problems　show　that　the　representation　capability　of　FRBM　model　is　significantly　better　than　the　traditional　RBM.　Additionally,　the　FRBM　also　reveals　better　robustness　property　compared　with　RBM　when　the　training　data　are　contaminated　by　noises.