Potential　drug-drug　interactions　(DDI)　can　lead　to　adverse　drug　reactions　(ADR),　and　DDI　prediction　can　help　pharmacy　researchers　detect　harmful　DDI　early.　However,　existing　DDI　prediction　methods　fall　short　in　fully　capturing　drug　information.　They　typically　employ　a　single-view　input,　focusing　solely　on　drug　features　or　drug　networks.　Moreover,　they　rely　exclusively　on　the　final　model　layer　for　predictions,　overlooking　the　nuanced　information　present　across　various　network　layers.　To　address　these　limitations,　we　propose　a　multi-scale　dual-view　fusion　(MSDF)　method　for　DDI　prediction.　More　specifically,　MSDF　first　constructs　two　views,　topological　and　feature　views　of　drugs,　as　model　inputs.　Then　a　graph　convolutional　neural　network　is　used　to　extract　the　feature　representations　from　each　view.　On　top　of　that,　a　multi-scale　fusion　module　integrates　information　across　different　graph　convolutional　layers　to　create　comprehensive　drug　embeddings.　The　embeddings　from　the　two　views　are　summed　as　the　final　representation　for　classification.　Experiments　on　two　real-world　datasets　demonstrate　that　MSDF　achieves　higher　accuracy　than　state-of-the-art　methods,　as　the　dual-view,　multi-scale　approach　better　captures　drug　characteristics.