Graph　embedding　has　been　extensively　studied　in　the　literature　and　is　widely　used　in　various　applications　such　as　drug　discovery,　social　network　analysis,　and　natural　language　processing.　However,　existing　approaches　ignore　the　attribute　information　or　are　limited　to　learning　graph　representations　at　certain　graph　scales　without　considering　the　layer-wise　community　structure　to　improve　embedding　learning.　To　tackle　these　problems,　we　propose　a　Community-aware　graph　Embedding　method　with　Multi-level　attribute　integration,　a　novel　attributed　graph　embedding　framework.　It　first　coarsens　the　topological　structure　and　attributes　information　alternating　in　a　parallel　strategy.　Then　in　each　coarsened　layer,　a　self-attention　mechanism　is　adopted　to　naturally　integrate　node　attributes　and　obtain　node　embedding　representations.　Next,　we　introduce　neighbor　propagation　at　the　same　layer,　cross-layer　community　propagation,　and　consider　community　information　of　nodes　in　the　coarsening　process　to　modify　the　embedding　representation.　Compared　with　the　previous　graph　embedding　methods,　experimental　results　in　different　application　scenarios　on　real-world　datasets　demonstrate　the　effectiveness　of　our　proposed　algorithm.