Attributed　graph　clustering　with　auto-encoder　(AE)　and　graph　convolutional　network　(GCN)　has　achieved　promising　performance　by　fusing　node　attribute　feature　and　structural　graph　information.　However,　there　are　some　limitations:　(i)　structural　information　from　pre-defined　graph　is　inaccurate　and　insufficient　for　graph　representation　learning;　(ii)　graph　embedding　of　last　layer　only　contains　partial　information　for　clustering　which　inevitably　deteriorates　clustering　performance.　To　address　these　issues,　we　propose　the　Improved　Attributed　Graph　Clustering　method　with　Representation　and　Structure　Augmentation　(IAGC-RSA).　The　representation　augmentor　with　multi-scale　and　multi-source　representation　attention　fusion　and　structure　augmentor　with　adaptive　graph　learning　are　designed　for　information　augmentation　from　structure　level　and　feature　level.　Thus,　IAGC-RSA　could　learn　a　more　comprehensive　and　discriminative　graph　embedding　representation　for　subsequent　clustering　task.　Experimental　results　conducted　on　some　benchmark　datasets　demonstrate　the　effectiveness　of　IAGC-RSA　for　node　clustering　task.