Hemodynamic　parameters　are　of　great　significance　in　the　clinical　diagnosis　and　treatment　of　cardiovascular　diseases.　However,　noninvasive,　real-time　and　accurate　acquisition　of　hemodynamics　remains　a　challenge　for　current　invasive　detection　and　simulation　algorithms.　Here,　we　integrate　computational　fluid　dynamics　with　our　customized　analysis　framework　based　on　a　multi-attribute　point　cloud　dataset　and　physics-informed　neural　networks　(PINNs)-aided　deep　learning　modules.　This　combination　is　implemented　by　our　workflow　that　generates　flow　field　datasets　within　two　types　of　patient　personalized　models　-　aorta　with　fine　coronary　branches　and　abdominal　aorta.　Deep　learning　modules　with　or　without　an　antecedent　hierarchical　structure　model　the　flow　field　development　and　complete　the　mapping　from　spatial　and　temporal　dimensions　to　4D　hemodynamics.　88,000　cases　on　4　randomized　partitions　in　16　controlled　trials　reveal　the　hemodynamic　landscape　of　spatiotemporal　anisotropy　within　two　types　of　personalized　models,　which　demonstrates　the　effectiveness　of　PINN　in　predicting　the　space-time　behavior　of　flow　fields　and　gives　the　optimal　deep　learning　framework　for　different　blood　vessels　in　terms　of　balancing　the　training　cost　and　accuracy　dimensions.　The　proposed　framework　shows　intentional　performance　in　computational　cost,　accuracy　and　visualization　compared　to　currently　prevalent　methods,　and　has　the　potential　for　generalization　to　model　flow　fields　and　corresponding　clinical　metrics　within　vessels　at　different　locations.　We　expect　our　framework　to　push　the　4D　hemodynamic　predictions　to　the　real-time　level,　and　in　statistically　significant　fashion,　applicable　to　morphologically　variable　vessels.