Quad-polarimetric　SAR　data　has　been　proved　to　be　useful　for　marine　oil　spill　classification.　Different　SAR　polarimetric　features　have　been　proposed　to　discriminate　between　oil　spills　and　look-alikes　which　could　cause　false　detection.　In　this　paper　we　explored　the　ability　of　convolutional　neural　network　(CNN)　in　automatic　oil　spill　classification,　by　taking　the　advantage　of　H/A/Alpha　polarimetric　decomposition　features　and　co-polarized　correlation　coefficients(CC).　The　convolutional　neural　network　(CNN)　was　refined　to　realize　the　classification,　in　which　global　average　pooling　layer　is　applied　instead　of　full　connection　layer.　The　quad-polarimetric　Radarsat-2　data　acquired　during　the　Norwegian　oil-on-water　exercise　was　tested　in　the　experiment.　Sea　surface　was　classified　as　clean　sea,　oil　spill,　look-alikes(biological　oil　spill　in　this　case),　and　emulsion.　The　experiment　results　show　that　H/A/Alpha　parameters　and　the　combination　of　H/A/Alpha　and　co-polarized　CC　obtained　higher　accuracy,　and　the　refined　CNN　has　better　performance　than　the　traditional　one　in　terms　of　accuracy　and　efficiency.