Coronary　artery　stenosis　resistance　(SR)　is　a　key　factor　for　noninvasive　calculations　of　fractional　flow　reserve　derived　from　coronary　CT　angiography　(FFRCT).　Existing　computational　fluid　dynamics　(CFD)　methods,　including　three-dimensional　(3-D)　computational　and　zero-dimensional　(0-D)　analytical　models,　are　usually　limited　by　high　calculation　cost　or　low　precision.　In　this　study,　we　have　developed　a　multi-input　back-propagation　neural　network　(BPNN)　that　can　rapidly　and　accurately　predict　coronary　SR.　A　training　data　set　including　3,028　idealized　anatomic　coronary　artery　stenosis　models　was　constructed　for　3-D　CFD　calculation　of　SR　with　specific　blood　flow　boundaries.　Based　on　3-D　calculation　results,　we　established　a　BPNN　whose　input　is　geometric　parameters　and　blood　flow,　whereas　output　is　SR.　Then,　a　test　set　(324　cases)　was　constructed　to　evaluate　the　performance　of　the　BPNN　model.　To　verify　the　validity　and　practicability　of　the　network,　BPNN　prediction　results　were　compared　with　3-D　CFD　and　O-D　analytical　model　results　from　patient-specific　models.　For　test　set,　the　mean　square　error　(MSE)　between　CFD　and　prediction　results　was　2.97%,　linear　regression　analysis　indicating　a　good　correlation　between　the　two　(P　＜　0.001).　For　30　patient-specific　models,　the　MSE　of　BPNN　and　the　0-D　model　were　3.26　and　9.7%,　respectively.　The　calculation　time　for　BPNN　and　the　3-D　CFD　model　for　30　cases　was　about　2.15　s　and　2　h,　respectively.　The　present　results　demonstrate　the　practicability　of　using　deep　learning　methods　for　fast　and　accurate　predictions　of　coronary　artery　SR.　Our　study　represents　an　advance　in　noninvasive　calculations　of　FFRCT.　NEW　&　NOTEWORTHY　This　study　developed　a　multi-input　back-propagation　neural　network　(BPNN)　that　can　be　used　to　predict　coronary　artery　stenosis　resistance　by　inputting　vascular　geometric　parameters　and　blood　flow.　Compared　with　previous　studies,　the　network　developed　in　this　study　can　accurately　and　rapidly　predict　coronary　artery　stenosis　resistance,　which　can　not　only　meet　clinical　requirements　but　also　reduce　the　cost　of　calculation　duration.　This　study　contributes　to　the　noninvasive　methods　for　the　numerical　calculation　of　fractional　flow　reserve　derived　from　coronary　CT　angiography　(FFRCT)　and　indicates　that　this　technique　can　potentially　be　used　for　evaluating　myocardial　ischemia.