With　the　fast　development　of　intelligent　traffic,　the　license　plate　recognition　technology　progressively　improves.　Most　of　existing　license　plate　recognition　techniques　can　well　recognize　character　information　for　single-row　license　plates　but　the　recognition　accuracies　for　double-row　license　plates　are　not　ideal　and　even　less　algorithms　support　Chinese　characters.　This　paper　introduces　a　double-row　license　plate　segmentation　method　with　CNN,　enabling　efficient　double-row　license　plate　recognition　for　originally　single-row　recognition　algorithms.　First,　this　method　trains　a　multi-label　classification　model　with　the　image　features　extracted　using　CNN.　Then,　we　use　the　model　to　automatically　segment　a　double-row　license　plate　into　two　single-row　license　plates.　In　addition,　we　have　constructed　a　training　and　validation　dataset　containing　more　than　200　000　Chinese　license　plate　images.　The　experimental　results　show　that　the　proposed　method　has　a　higher　accuracy　in　automatic　segmentation　of　double-row　license　plate,　thus　effectively　improving　the　accuracy　of　double-row　license　plate　recognition.　©　2019,　Beijing　China　Science　Journal　Publishing　Co.　Ltd.　All　right　reserved.