In　real　traffic　scenes,　the　quality　of　captured　images　are　generally　low　due　to　some　factors　such　as　lighting　conditions,　and　occlusion　on.　All　of　these　factors　are　challengeable　for　automated　recognition　algorithms　of　traffic　signs.　Deep　learning　has　provided　a　new　way　to　solve　this　kind　of　problems　recently.　The　deep　network　can　automatically　learn　features　from　a　large　number　of　data　samples　and　obtain　an　excellent　recognition　performance.　We　therefore　approach　this　task　of　recognition　of　traffic　signs　as　a　general　vision　problem,　with　few　assumptions　related　to　road　signs.　We　propose　a　model　of　Convolutional　Neural　Network　(CNN)　and　apply　the　model　to　the　task　of　traffic　signs　recognition.　The　proposed　model　adopts　deep　CNN　as　the　supervised　learning　model,　directly　takes　the　collected　traffic　signs　image　as　the　input,　alternates　the　convolutional　layer　and　subsampling　layer,　and　automatically　extracts　the　features　for　the　recognition　of　the　traffic　signs　images.　The　proposed　model　includes　an　input　layer,　three　convolutional　layers,　three　subsampling　layers,　a　fully-connected　layer,　and　an　output　layer.　To　validate　the　proposed　model,　the　experiments　are　implemented　using　the　public　dataset　of　China　competition　of　fuzzy　image　processing.　Experimental　results　show　that　the　proposed　model　produces　a　recognition　accuracy　of　99.01　%　on　the　training　dataset,　and　yield　a　record　of　92%　on　the　preliminary　contest　within　the　fourth　best.