With　the　deeper　research　on　attributed　networks,　graph　anomaly　detection　is　becoming　an　increasingly　important　topic.　It　aims　to　identify　patterns　deviating　from　a　majority　of　nodes.　Currently,　graph　anomaly　detection　algorithms　based　on　reconstruction-based　learning　and　contrastive-based　learning　have　gained　significant　attention.　To　harness　diverse　supervised　signals,　an　intuitive　approach　is　to　find　an　elegant　strategy　to　fuse　these　two　paradigms,　forming　the　hybrid　learning　paradigm.　Despite　the　success　of　the　hybrid　learning　paradigm,　due　to　its　subgraph　sampling　based　approach,　it　still　grapples　with　issues　related　to　unreliable　neighborhood　information　and　the　neglect　of　topological　details.　To　address　these　limitations,　this　paper　proposes　a　new　hybrid　learning　paradigm　via　multi-view　discriminative　awareness　learning　for　graph　anomaly　detection.　Unlike　the　previous　hybrid　learning　paradigm,　the　graph　reconstruction　module　fully　incorporates　attribute　and　topology　information,　enhancing　the　comprehensiveness　of　data　reconstruction.　Moreover,　the　multi-view　discrimination　module　employs　a　view-level　contrast　method　based　on　the　complete　graph,　which　helps　to　comprehensively　extract　the　information　in　the　attributed　network　and　mitigates　the　neighborhood　unreliability　without　increasing　the　complexity.　The　experimental　results,　obtained　from　a　rigorous　evaluation　on　six　benchmark　datasets,　demonstrate　the　effectiveness　of　the　proposed　method　compared　to　existing　baseline　methods.