Hemodynamic　prediction　of　carotid　artery　stenosis　(CAS)　is　of　great　clinical　significance　in　the　diagnosis,　prevention,　and　treatment　prognosis　of　ischemic　strokes.　While　computational　fluid　dynamics　(CFD)　is　recognized　as　a　useful　tool,　it　shows　a　crucial　issue　that　the　high　computational　costs　are　usually　required　for　real-time　simulations　of　complex　blood　flows.　Given　the　powerful　feature-extraction　capabilities,　the　deep　learning　(DL)　methodology　has　a　high　potential　to　implement　the　mapping　of　anatomic　geometries　and　CFD-driven　flow　fields,　which　enables　accomplishing　fast　and　accurate　hemodynamic　prediction　for　clinical　applications.　Based　on　a　brain/neck　CT　angiography　database　of　280　subjects,　image　based　three-dimensional　CFD　models　of　CAS　were　constructed　through　blood　vessel　extraction,　computational　domain　meshing　and　setting　of　the　pulsatile　flow　boundary　conditions;　a　series　of　CFD　simulations　were　undertaken.　A　DL　strategy　was　proposed　and　accomplished　in　terms　of　point　cloud　datasets　and　a　DL　network　with　dual　sampling-analysis　channels.　This　enables　multimode　mapping　to　construct　the　image-based　geometries　of　CAS　while　predicting　CFD-based　hemodynamics　based　on　training　and　testing　datasets.　The　CFD　simulation　was　validated　with　the　mass　flow　rates　at　two　outlets　reasonably　agreed　with　the　published　results.　Comprehensive　analysis　and　error　evaluation　revealed　that　the　DL　strategy　enables　uncovering　the　association　between　transient　blood　flow　characteristics　and　artery　cavity　geometric　information　before　and　after　surgical　treatments　of　CAS.　Compared　with　other　methods,　our　DL-based　model　trained　with　more　clinical　data　can　reduce　the　computational　cost　by　7,200　times,　while　still　demonstrating　good　accuracy　(error　＜　12.5%)　and　flow　visualization　in　predicting　the　two　hemodynamic　parameters.　In　addition,　the　DL-based　predictions　were　in　good　agreement　with　CFD　simulations　in　terms　of　mean　velocity　in　the　stenotic　region　for　both　the　preoperative　and　postoperative　datasets.　This　study　points　to　the　capability　and　significance　of　the　DL-based　fast　and　accurate　hemodynamic　prediction　of　preoperative　and　postoperative　CAS.　For　accomplishing　real-time　monitoring　of　surgical　treatments,　further　improvements　in　the　prediction　accuracy　and　flexibility　may　be　conducted　by　utilizing　larger　datasets　with　specific　real　surgical　events　such　as　stent　intervention,　adopting　personalized　boundary　conditions,　and　optimizing　the　DL　network.