Recently,　several　in-loop　filtering　algorithms　based　on　convolutional　neural　network　(CNN)　have　been　proposed　to　improve　the　efficiency　of　HEVC　(High　Efficiency　Video　Coding).　Conventional　CNN-based　filters　only　apply　a　single　model　to　the　whole　image,　which　cannot　adapt　well　to　all　local　features　from　the　image.　To　solve　this　problem,　an　in-loop　filtering　algorithm　based　on　a　dynamic　convolutional　capsule　network　(DCC-net)　is　proposed,　which　embeds　localized　dynamic　routing　and　dynamic　segmentation　algorithms　into　capsule　network,　and　integrate　them　into　the　HEVC　hybrid　video　coding　framework　as　a　new　in-loop　filter.　The　proposed　method　brings　average　7.9%　and　5.9%　BD-BR　reductions　under　all　intra　(AI)　and　random　access　(RA)　configurations,　respectively,　as　well　as,　0.4　dB　and　0.2　dB　BD-PSNR　gains,　respectively.　In　addition,　the　proposed　algorithm　has　an　outstanding　performance　in　terms　of　time　efficiency.