The　graph　neural　network　(GNN)　has　emerged　as　an　effective　way　to　mine　relationships　between　data　due　to　its　powerful　modeling　capability　for　graph　structure　data,　and　it　has　garnered　significant　attention　from　researchers　for　intelligent　fault　diagnosis　tasks.　However,　the　adjacency　matrix　of　most　GNN　models　with　deep　architecture　is　always　fixed　during　the　aggregation　process,　and　the　edge　connection　relationship　cannot　be　adaptively　adjusted,　which　limits　their　performance　for　feature　representation.　Moreover,　for　few-shot　diagnosis　scenarios,　the　generalization　performance　of　deep　GNN　models　will　be　further　degraded　due　to　fixed　receptive　fields　and　limited　training　samples.　To　address　these　issues,　a　deep　adaptively　dynamic　edge　graph　convolution　network　(DADE-GCN)　with　attention　weight　and　a　high-dimension　affinity　feature　graph　is　proposed.　First,　a　deep　adaptively　dynamic　edge　graph　convolutional　module　with　attention　weight　(DADE-GCNWAW)　is　developed　to　dynamically　adjust　the　receptive　field　in　different　graph　convolution　layers.　Subsequently,　the　output　features　of　different　layers　are　fused　by　a　self-attention　mechanism.　Second,　to　overcome　the　effect　of　the　time-shift　problem　existing　in　vibration　signals　and　capture　accurate　interdependencies　between　data,　a　high-dimension　affinity　feature　graph　construction　method　is　proposed　to　construct　graph　structure　data.　The　effectiveness　of　the　proposed　method　is　quantitatively　verified　by　two　rotating　machinery　datasets,　which　indicate　that　the　proposed　DADE-GCN　model　can　achieve　average　diagnosis　accuracies　of　98.80%　in　both　the　two　few-shot　diagnosis　tasks,　which　outperform　several　state-of-the-art　recognition　methods.