Segment　Routing　(SR)　is　a　source　routing　paradigm　which　is　widely　used　in　Traffic　Engineering　(TE).　By　using　SR,　a　node　steers　a　packet　through　an　ordered　list　of　instructions　called　segments.　By　some　extensions　of　interior　gateway　protocol,　SR　can　be　applied　to　IP/MPLS　or　IPv6　network　without　signal　protocol.　SR　over　IPv6　(SRv6)　is　attracting　wide　attention　because　of　its　interoperation　ability　with　IPv6.　However,　upgrading　the　existing　IPv6　network　directly　to　a　full　SRv6　one　can　be　difficult,　because　large-scale　equipment　replacement　or　software　upgrade　may　cause　economic　and　technical　problems.　TE　in　partially　deployed　SR　network　is　becoming　a　hot　research　topic.　In　this　paper,　we　propose　the　TE　algorithm　Weight　Adjustment-SRTE　(WA-SRTE)　in　partially　deployed　SRv6　network,　in　which　SRv6　capable　nodes　are　dispersedly　deployed.　Our　objective　is　to　minimize　the　network＇s　maximum　link　utilization.　WA-SRTE　converts　the　TE　problem　into　a　Deep　Reinforcement　Learning　problem　and　optimizes　the　OSPF　weight,　SRv6　node　deployment　and　traffic　paths　simultaneously.　Besides,　traffic　variation　is　also　considered　and　we　use　a　representative　Traffic　Matrix　(TM)　to　epitomize　the　traffic　characteristics　over　a　period　of　time.　Experiments　demonstrate　that　with　20%　to　40%　of　the　SRv6　nodes　deployed,　we　can　achieve　TE　performance　as　good　as　in　a　full　SR　network　for　the　experiment　topologies.　The　results　with　WA　remarkably　outperform　the　results　without　it.　Our　algorithm　also　gets　near-optimal　results　with　changing　traffic.