Aiming　at　the　sequential　collaborative　optimization　problem　of　the　electric　power　and　traffic　network　integration　network　(IETN)　with　dynamic　interaction　of　power　distribution　system,　transportation　system　and　electric　vehicle　under　multiple　uncertain　factors,　a　collaborative　optimization　method　for　IETN　based　on　graph　neural　network　multi-agent　reinforcement　learning　is　proposed　to　solve　the　above　problems.　Firstly,　the　interaction　relationship　among　electric　vehicles　is　converted　into　a　dynamic　network　graph　model　based　on　the　graph　theory.　A　graph　neural　network　multi-agent　reinforcement　learning　algorithm　based　on　attention　mechanism　is　proposed　to　solve　the　charging　navigation　strategy　of　electric　vehicles,　so　as　to　explore　the　mutual　influence　among　electric　vehicles.　Then,　the　optimal　power　flow　of　distribution　network　considering　the　renewable　energy　output　in　an　active　distribution　network　is　solved,　and　the　marginal　price　of　distribution　network　node　is　obtained　by　the　second-order　cone　optimization　and　dual　optimization　theory,　so　as　to　study　the　dynamic　interaction　characteristics　of　power　and　transportation　systems.　Finally,　the　simulation　verification　is　carried　out　on　a　108-node　traffic　network　and　the　IEEE　33-node　power　system　in　a　certain　area.　©　2023　Science　Press.　All　rights　reserved.