The　quantity　of　wind　and　photovoltaic　power-based　distributed　generators　(DGs)　is　continually　rising　within　the　distribution　network,　presenting　obstacles　to　its　safe,　steady,　and　cost-effective　functioning.　Active　distribution　network　dynamic　reconfiguration　(ADNDR)　improves　the　consumption　rate　of　renewable　energy,　reduces　line　losses,　and　optimizes　voltage　quality　by　optimizing　the　distribution　network　structure.　Despite　being　formulated　as　a　highly　dimensional　and　combinatorial　nonconvex　stochastic　programming　task,　conventional　model-based　solvers　often　suffer　from　computational　inefficiency　and　approximation　errors,　whereas　population-based　search　methods　frequently　exhibit　premature　convergence　to　suboptimal　solutions.　Moreover,　when　dealing　with　high-dimensional　ADNDR　problems,　these　algorithms　often　face　modeling　difficulties　due　to　their　large　scale.　Deep　reinforcement　learning　algorithms　can　effectively　solve　the　problems　above.　Therefore,　by　combining　the　graph　attention　network　(GAT)　with　the　deep　deterministic　policy　gradient　(DDPG)　algorithm,　a　method　based　on　the　graph　attention　network　deep　deterministic　policy　gradient　(GATDDPG)　algorithm　is　proposed　to　online　solve　the　ADNDR　problem　with　the　uncertain　outputs　of　DGs　and　loads.　Firstly,　considering　the　uncertainty　in　distributed　power　generation　outputs　and　loads,　a　nonlinear　stochastic　optimization　mathematical　model　for　ADNDR　is　constructed.　Secondly,　to　mitigate　the　dimensionality　of　the　decision　space　in　ADNDR,　a　cyclic　topology　encoding　mechanism　is　implemented,　which　leverages　graph-theoretic　principles　to　reformulate　the　grid　infrastructure　as　an　adaptive　structural　mapping　characterized　by　time-varying　node-edge　interactions　Furthermore,　the　GATDDPG　method　proposed　in　this　paper　is　used　to　solve　the　ADNDR　problem.　The　GAT　is　employed　to　extract　characteristics　pertaining　to　the　distribution　network　state,　while　the　DDPG　serves　the　purpose　of　enhancing　the　process　of　reconfiguration　decision-making.　This　collaboration　aims　to　ensure　the　safe,　stable,　and　cost-effective　operation　of　the　distribution　network.　Finally,　we　verified　the　effectiveness　of　our　method　using　an　enhanced　IEEE　33-bus　power　system　model.　The　outcomes　of　the　simulations　demonstrate　its　capacity　to　significantly　enhance　the　economic　performance　and　stability　of　the　distribution　network,　thereby　affirming　the　proposed　method＇s　effectiveness　in　this　study.