The　emergence　of　Segment　Routing　(SR)　provides　a　novel　routing　paradigm　that　uses　a　routing　technique　called　source　packet　routing.　In　SR　architecture,　the　paths　that　the　packets　choose　to　route　on　are　indicated　at　the　ingress　router.　Compared　with　shortest-path-based　routing　in　traditional　distributed　routing　protocols,　SR　can　realize　a　flexible　routing　by　implementing　an　arbitrary　flow　splitting　at　the　ingress　router.　Despite　the　advantages　of　SR,　it　may　be　difficult　to　update　the　existing　IP　network　to　a　full　SR　deployed　network,　for　economical　and　technical　reasons.　Updating　partial　of　the　traditional　IP　network　to　the　SR　network,　thus　forming　a　hybrid　SR　network,　is　a　preferable　choice.　For　the　traffic　is　dynamically　changing　in　a　daily　time,　in　this　paper,　we　propose　a　Weight　Adjustment　algorithm　WASAR　to　optimize　routing　in　a　dynamic　hybrid　SR　network.　WASAR　algorithm　can　be　divided　into　three　steps:　firstly,　representative　Traffic　Matrices　(TMs)　and　the　expected　TM　are　obtained　from　the　historical　TMs　through　ultrascalable　spectral　clustering　algorithm.　Secondly,　given　the　network　topology,　the　initial　network　weight　setting　and　the　expected　TM,　we　can　realize　the　link　weight　optimization　and　SR　node　deployment　optimization　through　a　Deep　Reinforcement　Learning　(DRL)　algorithm.　Thirdly,　we　optimize　the　flow　splitting　ratios　of　SR　nodes　in　a　centralized　online　manner　under　dynamic　traffic　demands,　in　order　to　improve　the　network　performance.　In　the　evaluation,　we　exploit　historical　TMs　to　test　the　performance　of　the　obtained　routing　configuration　in　WASAR.　The　extensive　experimental　results　validate　that　our　proposed　WASAR　algorithm　has　superior　performance　in　reducing　Maximum　Link　Utilization　(MLU)　under　the　dynamic　traffic.