Deep　learning　is　recently　regarded　as　the　closest　artificial　intelligence　model　to　human　brain.　It　is　about　learning　multiple　levels　of　representation　and　abstraction　that　help　to　make　sense　of　data　such　as　images,　sound,　and　text.　One　deep　model　often　consists　of　a　hierarchical　architecture　that　has　the　capability　to　model　super　non-linear　and　stochastic　problems.　Restricted　Boltzmann　Machine　(RBM)　is　the　main　constructing　block　of　current　deep　networks,　as　most　of　deep　architectures　are　built　with　it.　Based　on　MapReduce　framework　and　Hadoop　distributed　file　system,　this　paper　proposes　a　distributed　algorithm　for　training　the　RBM　model.　Its　implementation　and　performance　are　evaluated　on　Big　Data　platform-Hadoop.　The　main　contribution　of　the　new　learning　algorithm　is　that　it　solves　the　scalability　problem　that　limits　the　development　of　deep　learning.　The　intelligence　growing　process　of　human　brain　requires　learning　from　Big　Data.　The　distributed　learning　mechanism　for　RBM　makes　it　possible　to　abstract　sophisticated　and　informative　features　from　Big　Data　to　achieve　high-level　intelligence.　The　evaluations　of　the　proposed　learning　algorithm　are　carried　out　on　image　inpainting　and　classification　problems　based　on　the　BAS　dataset　and　MNIST　hand-written　digits　dataset.　©　2016　Elsevier　B.V.