As　one　of　the　research　hotspots　in　recent　years,　especially　in　pattern　recognition,　Convolutional　Neural　Network　(CNN)　is　widely　known　for　its　high　efficiency.　However　some　researches　show　that　there　is　a　problem　in　the　CNN　which　cannot　learn　the　high-level　features.　In　order　to　solve　this　problem,　this　paper　proposes　a　new　kind　of　image　representation,　which　we　call　it　＂shape　encoding　maps＂.　Our　experimental　results　show　that,　in　most　cases,　the　recognition　accuracies　obtained　by　inputting　the　shape　encoded　maps　to　a　CNN　are　higher　than　that　of　using　the　original　image　data　for　a　CNN　to　learn　directly　without　shape　encoding.