The　recent　development　of　marine　transportation　and　offshore　oil　exploration　and　exploitation　has　increased　the　risk　of　marine　oil　spill　accidents.　Oil　pollution　is　one　of　the　most　complex　marine　pollutions,　it　will　seriously　threaten　the　marine　ecological　environment.　Synthetic　aperture　radar　(SAR)　has　been　widely　used　in　marine　monitoring　for　its　all-day　and　all-weather　imaging　capability.　Polarimetric　SAR　can　obtain　polarimetric　scattering　information　of　the　ground　targets,　which　provides　a　new　means　for　marine　oil　spill　detection.　Besides,　with　the　recent　development　of　machine　learning　algorithms,　especially　convolutional　neural　networks,　higher　oil　spill　classification　accuracy　can　be　obtained　given　more　training　datasets.　However,　currently　most　neural　network　models　are　applied　to　real-valued　input　and　cannot　fully　exploit　the　phase　information　contained　in　complex-valued　data　of　polarimetric　SAR　images.　In　this　study,　a　classification　approach　of　marine　oil　spills　in　polarimetric　SAR　images　is　presented　based　on　the　complex-valued　convolutional　neural　network　(CVCNN).　The　experimental　results　show　that　the　proposed　approach　outperforms　the　real-valued　convolutional　neural　network　(RVCNN)　in　both　the　detection　of　oil　spills　from　sea　surface　and　the　classification　of　crude　oil　films　and　biogenic　oil　films.