Although　the　citations　between　scientific　documents　are　deemed　as　a　vehicle　for　dissemination,　inheritance　and　development　of　scientific　knowledge,　not　all　citations　are　well-positioned　to　be　equal.　A　plethora　of　taxonomies　and　machine-learning　models　have　been　implemented　to　tackle　the　task　of　citation　function　and　importance　classification　from　qualitative　aspect.　Inspired　by　the　success　of　kernel　functions　from　resulting　general　models　to　promote　the　performance　of　the　support　vector　machine　(SVM)　model,　this　work　exploits　the　potential　of　combining　generative　and　discriminative　models　for　the　task　of　citation　importance　classification.　In　more　detail,　generative　features　are　generated　from　a　topic　model,　citation　influence　model　(CIM)　and　then　fed　to　two　discriminative　traditional　machine-learning　models,　SVM　and　RF　(random　forest),　and　a　deep　learning　model,　convolutional　neural　network　(CNN),　with　other　13　traditional　features　to　identify　important　citations.　The　extensive　experiments　are　performed　on　two　data　sets　with　different　characteristics.　These　three　models　perform　better　on　the　data　set　from　one　discipline.　It　is　very　possible　that　the　patterns　for　important　citations　may　vary　by　the　fields,　which　disable　machine-learning　models　to　learn　effectively　the　discriminative　patterns　from　publications　from　multiple　domains.　The　RF　classifier　outperforms　the　SVM　classifier,　which　accords　with　many　prior　studies.　However,　the　CNN　model　does　not　achieve　the　desired　performance　due　to　small-scaled　data　set.　Furthermore,　our　CIM　model-based　features　improve　further　the　performance　for　identifying　important　citations.