The　interventional　treatment　of　cerebral　aneurysm　requires　hemodynamics　to　provide　proper　guidance.　Computational　fluid　dynamics　(CFD)　is　gradually　used　in　calculating　cerebral　aneurysm　hemodynamics　before　and　after　flow-diverting　(FD)　stent　placement.　However,　the　complex　operation　(such　as　the　construction　and　placement　simulation　of　fully　resolved　or　porous-medium　FD　stent)　and　high　computational　cost　of　CFD　hinder　its　application.　To　solve　these　problems,　we　applied　aneurysm　hemodynamics　point　cloud　data　sets　and　a　deep　learning　network　with　double　input　and　sampling　channels.　The　flexible　point　cloud　format　can　represent　the　geometry　and　flow　distribution　of　different　aneurysms　before　and　after　FD　stent　(represented　by　porous　medium　layer)　placement　with　high　resolution.　The　proposed　network　can　directly　analyze　the　relationship　between　aneurysm　geometry　and　internal　hemodynamics,　to　further　realize　the　flow　field　prediction　and　avoid　the　complex　operation　of　CFD.　Statistical　analysis　shows　that　the　prediction　results　of　hemodynamics　by　our　deep　learning　method　are　consistent　with　the　CFD　method　(error　function　＜13%),　but　the　calculation　time　is　significantly　reduced　1,800　times.　This　study　develops　a　novel　deep　learning　method　that　can　accurately　predict　the　hemodynamics　of　different　cerebral　aneurysms　before　and　after　FD　stent　placement　with　low　computational　cost　and　simple　operation　processes.