Reinforcement　learning,　as　an　efficient　method　for　solving　uncertainty　decision　making　in　power　systems,　is　widely　used　in　multi-stage　stochastic　power　dispatch　and　dynamic　optimization.　However,　the　low　generalization　and　practicality　of　traditional　reinforcement　learning　algorithms　limit　their　online　application.　The　dispatch　strategy　learned　offline　can　only　adapt　to　specific　scenarios,　and　its　policy　performance　degrades　significantly　if　the　sample　drastically　change　or　the　topology　variation.　To　fill　these　gaps,　a　novel　contextual　meta　graph　reinforcement　learning　(Meta-GRL)　method　a　more　general　contextual　Markov　decision　process　(CMDP)　modeling　are　proposed.　The　proposed　Meta-GRL　adopts　CMDP　scheme　and　graph　representation,　extracts　and　encodes　the　differentiated　scene　context,　and　can　be　extended　to　various　scene　changes.　The　upper　meta-learner　embedded　in　context　in　Meta-GRL　is　proposed　to　realize　scene　recognition.　While　the　lower　base-earner　is　guided　to　learn　generalized　context-specified　policy.　The　test　results　in　IEEE39　and　open　environment　show　that　the　Meta-GRL　achieves　more　than　90%　optimization　and　entire　period　applicability　under　the　premise　of　saving　computing　resources.