Time　series　classification　is　one　of　the　most　important　and　challenging　problems　in　the　field　of　data　mining.　This　paper　presents　a　time　series　classification　model　named　FusionNet,　which　is　based　on　multi-scale　dynamic　convolutional　features　and　distance　features.　The　main　contribution　of　this　paper　includes　three　aspects:　firstly,　based　on　the　multi-scale　dynamic　convolution　operation,　we　propose　a　Multi-Scale　Dynamic　Convolution　Network　(MSDCNet).　It　uses　multi-scale　dynamic　convolution　to　dynamically　adjust　the　convolutional　kernels　for　different　input　data　and　extract　the　features　of　time　series.　Secondly,　by　calculating　the　distance　between　the　prototypes　and　the　embedding　vectors,　we　construct　a　Prototype　Network　(PrototypeNet)　to　extract　the　distance　features　of　time　series.　At　the　same　time,　we　design　a　distance　loss　to　ensure　to　calculate　the　effective　distance　features.　Finally,　we　fuse　the　multi-scale　dynamic　convolution　features　with　the　distance　features　to　obtain　the　fusing　features　for　classification.　Experimental　results　on　44　UCR　datasets　show　that　the　proposed　FusionNet　achieves　better　results　on　multiple　datasets　than　the　previous　model,　demonstrating　its　effectiveness.　©　2021　IEEE.