As　an　essential　element　in　the　overall　designing　of　a　service　system,　service　planning　plays　an　important　role　in　improving　final　service　quality　and　user　experience.　Service　planning　is　a　customer-oriented　approach　that　facilitates　the　translation　of　customer　requirements　into　activity　process　features　or　engineering　characteristics.　The　trend　is　towards　learning　decision-support　knowledge　from　massive　service　case　data　to　facilitate　service　planning.　However,　when　learning　from　imbalanced　data,　existing　methods　have　poor　predictive　ability　to　identify　minority　patterns.　To　improve　the　quality　and　efficiency　of　the　service　planning,　an　innovative　approach　based　on　the　combination　of　granular　computing　and　deep　learning　are　presented.　It　employs　an　inductive　paradigm,　clustering　examples　in　a　best　granularity,　sampling　and　refining　into　a　more　balanced　example　set,　and　feeding　them　into　a　deep　learning　model.　The　proposed　approach　can　mine　the　planning　patterns　between　the　customer　requirements　and　service　process　features,　thereby　facilitating　knowledge　transferring　in　service　planning　under　the　social　manufacturing　context.　(C)　2017　Elsevier　B.V.　All　rights　reserved.