The　emerging　Graph　Convolutional　Networks　(GCNs)　have　attracted　widespread　attention　in　graph　learning,　due　to　their　good　ability　of　aggregating　the　information　between　higher-order　neighbors.　However,　real-world　graph　data　contains　high　noise　and　redundancy,　making　it　hard　for　GCNs　to　accurately　depict　the　complete　relationships　between　nodes,　which　seriously　degrades　the　quality　of　graph　representations.　Moreover,　existing　studies　commonly　ignore　the　distribution　difference　between　feature　and　semantic　spaces　in　graphs,　causing　inferior　model　generalization.　To　address　these　challenges,　we　propose　DIB-RGCN,　a　novel　robust　GCN　framework,　to　explore　the　optimal　graph　representation　with　the　guidance　of　the　well-designed　dual　information　bottleneck　principle.　First,　we　analyze　the　reasons　for　distribution　differences　and　theoretically　prove　that　minimal　sufficient　representations　in　specific　spaces　cannot　promise　optimal　performance　for　downstream　tasks.　Next,　we　design　new　dual　channels　to　regularize　feature　and　semantic　spaces,　eliminating　the　sharing　of　task-irrelevant　information　between　spaces.　Different　from　existing　denoising　algorithms　that　adopt　a　random　dropping　manner,　we　innovatively　replace　potential　noisy　features　and　edges　with　local　neighboring　representations.　This　design　lowers　edge-specific　coefficient　assignment,　alleviating　the　interference　of　original　representations　while　retaining　graph　structures.　Further,　we　maximize　the　sharing　of　task-relevant　information　between　feature　and　semantic　spaces　to　alleviate　the　difference　between　them.　Using　real-world　datasets,　extensive　experiments　demonstrate　the　robustness　of　the　proposed　DIB-RGCN,　which　outperforms　state-of-the-art　methods　on　classification　tasks.