Graph　convolutional　networks　(GCNs)　have　achieved　impressive　results　in　many　medical　scenarios　involving　graph　node　classification　tasks.　However,　there　are　difficulties　in　transfer　learning　for　graph　representation　learning　and　graph　network　models.　Most　GNNs　work　only　in　a　single　domain　and　cannot　transfer　the　learned　knowledge　to　other　domains.　Coronary　Heart　Disease　(CHD)　is　a　high-mortality　disease,　and　there　are　non-public　and　significant　differences　in　CHD　datasets　for　current　research,　which　makes　it　difficult　to　perform　unified　transfer　learning.　Therefore,　in　this　paper,　we　propose　a　novel　adversarial　domain-adaptive　multichannel　graph　convolutional　network　(DAMGCN)　that　can　perform　graph　transfer　learning　on　cross-domain　tasks　to　achieve　cross-domain　medical　knowledge　transfer　on　different　CHD　datasets.　First,　we　use　a　two-channel　GCN　model　for　feature　aggregation　using　local　consistency　and　global　consistency.　Then,　a　uniform　node　representation　is　generated　for　different　graphs　using　an　attention　mechanism.　Finally,　we　provide　a　domain　adversarial　module　to　decrease　the　discrepancies　between　the　source　and　target　domain　classifiers　and　optimize　the　three　loss　functions　in　order　to　accomplish　source　and　target　domain　knowledge　transfer.　The　experimental　findings　demonstrate　that　our　model　performs　best　on　three　CHD　datasets,　and　its　performance　is　greatly　enhanced　by　graph　transfer　learning.