The　clinical　treatment　planning　of　coronary　heart　disease　requires　hemodynamic　parameters　to　provide　proper　guidance.　Computational　fluid　dynamics　(CFD)　is　gradually　used　in　the　simulation　of　cardiovascular　hemodynamics.　However,　for　the　patient-specific　model,　the　complex　operation　and　high　computational　cost　of　CFD　hinder　its　clinical　application.　To　deal　with　these　problems,　we　develop　cardiovascular　hemodynamic　point　datasets　and　a　dual　sampling　channel　deep　learning　network,　which　can　analyze　and　reproduce　the　relationship　between　the　cardiovascular　geometry　and　internal　hemodynamics.　The　statistical　analysis　shows　that　the　hemodynamic　prediction　results　of　deep　learning　are　in　agreement　with　the　conventional　CFD　method,　but　the　calculation　time　is　reduced　600-fold.　In　terms　of　over　2　million　nodes,　prediction　accuracy　of　around　90%,　computational　efficiency　to　predict　cardiovascular　hemodynamics　within　1　second,　and　universality　for　evaluating　complex　arterial　system,　our　deep　learning　method　can　meet　the　needs　of　most　situations.　Anzai　et　al.　propose　a　deep　learning　approach　to　estimate　the　3D　hemodynamics　of　complex　aorta-coronary　artery　geometry　in　the　context　of　coronary　artery　bypass　surgery.　Their　method　reduces　the　calculation　time　600-fold,　while　allowing　high　resolution　and　similar　accuracy　as　traditional　computational　fluid　dynamics　(CFD)　method.