Graph　neural　networks　entail　massive　labeled　samples　for　training,　and　manual　labeling　generally　requires　unaffordable　costs.　Active　learning　has　emerged　as　a　promising　approach　to　selecting　a　smaller　set　of　informative　labeled　samples　to　improve　model　performance.　However,　few　active　learning　techniques　for　graph　data　account　for　the　cluster　structure　and　redundancy　of　samples.　To　address　these　issues,　we　propose　an　approach　that　employs　uncertain　information　as　an　observation　for　a　reinforcement　learning　agent　to　adaptively　learn　a　node　selection　policy.　We　construct　states　using　node　information　obtained　via　mutual　information,　which　considers　both　the　graph　structure　and　the　node　attributes.　The　proposed　method　accurately　quantifies　node　information　by　leveraging　the　receptive　field　of　the　graph　convolutional　network　and　capturing　the　clustering　structure　of　the　data,　taking　into　account　the　low　redundancy　and　diversity　of　the　labeled　samples.　Experiments　conducted　on　real-world　datasets　demonstrate　the　superiority　of　the　proposed　approach　over　several　state-of-the-art　methods.