Scene　recognition　is　a　significant　topic　in　computer　vision,　and　Deep　Boltzmann　Machines　(DBM)　is　a　state-of-the-art　deep　learning　model　which　has　been　widely　applied　in　object　and　hand　written　digit　recognition.　However,　when　the　DBM　is　used　in　scene　recognition,　it　is　difficult　to　handle　large　images　due　to　its　computational　complexity.　In　this　paper,　we　present　a　deep　learning　method　based　on　Convolutional　Neural　Networks　(CNN)　and　DBM　for　scene　image　recognition.　First,　in　order　to　categorize　large　images,　the　CNN　is　utilized　to　preprocess　images　for　dimensional　reduction.　Then,　regarding　the　preprocessed　images　as　the　input　of　the　visible　layer,　the　DBM　model　is　trained　using　Contrastive　Divergence　(CD)　algorithm.　Finally,　after　extracting　features　by　the　DBM,　the　softmax　regression　is　employed　to　perform　scene　recognition　tasks.　Since　the　CNN　can　reduce　effectively　image　size,　the　proposed　method　can　improve　the　computational　efficiency　and　becomes　more　suitable　for　large　image　recognition.　Experimental　evaluations　using　SIFT　Flow　dataset　and　fifteen-scene　dataset　demonstrate　that　the　proposed　method　can　obtain　promising　results.　©　2015　IEEE.