Attribute　graphs　are　a　crucial　data　structure　for　graph　communities.　However,　the　presence　of　redundancy　and　noise　in　the　attribute　graph　can　impair　the　aggregation　effect　of　integrating　two　different　heterogeneous　distributions　of　attribute　and　structural　features,　resulting　in　inconsistent　and　distorted　data　that　ultimately　compromises　the　accuracy　and　reliability　of　attribute　graph　learning.　For　instance,　redundant　or　irrelevant　attributes　can　result　in　overfitting,　while　noisy　attributes　can　lead　to　underfitting.　Similarly,　redundant　or　noisy　structural　features　can　affect　the　accuracy　of　graph　representations,　making　it　challenging　to　distinguish　between　different　nodes　or　communities.　To　address　these　issues,　we　propose　the　embedded　fusion　graph　auto-encoder　framework　for　self-supervised　learning　(SSL),　which　leverages　multitask　learning　to　fuse　node　features　across　different　tasks　to　reduce　redundancy.　The　embedding　fusion　graph　auto-encoder　(EFGAE)　framework　comprises　two　phases:　pretraining　(PT)　and　downstream　task　learning　(DTL).　During　the　PT　phase,　EFGAE　uses　a　graph　auto-encoder　(GAE)　based　on　adversarial　contrastive　learning　to　learn　structural　and　attribute　embeddings　separately　and　then　fuses　these　embeddings　to　obtain　a　representation　of　the　entire　graph.　During　the　DTL　phase,　we　introduce　an　adaptive　graph　convolutional　network　(AGCN),　which　is　applied　to　graph　neural　network　(GNN)　classifiers　to　enhance　recognition　for　downstream　tasks.　The　experimental　results　demonstrate　that　our　approach　outperforms　state-of-the-art　(SOTA)　techniques　in　terms　of　accuracy,　generalization　ability,　and　robustness.　IEEE