With　the　increase　of　data　centers,　the　number　of　disks　also　grows　rapidly.　Therefore,　the　prediction　of　disk　failures　has　become　an　important　task　for　both　academia　and　industry.　Existing　prediction　schemes　predict　disk　failure　in　the　short　prediction　horizon　or　with　a　short　time　window.　However,　these　schemes　cannot　achieve　ideal　performance　for　a　long　prediction　horizon　with　a　long　time　window.　In　this　paper,　we　proposed　a　deep　learning　method　that　can　effectively　solve　the　above　problems.　We　refine　the　Self-Monitoring,　Analysis　and　Reporting　Technology　(SMART)　attributes　by　using　information　entropy　to　select　the　most　related　attributes　for　prediction.　Moreover,　we　proposed　the　Multiple　Channel　Convolutional　Neural　Network　based　LSTM　(MCCNN-LSTM)　model　to　predict　whether　disk　failures　will　occur　in　a　given　disk　in　next　few　days.　We　further　evaluate　the　MCCNN-LSTM　model　by　comparing　it　with　the　state-of-the-art　works.　Extensive　experiments　show　that　our　model　can　improve　FDR　(Fault　Detection　Rate)　to　99.8%　and　reduce　FAR　(False　Alarm　Rate)　to　0.2%.