Background　and　objective:　The　functional　assessment　of　the　severity　of　coronary　stenosis　from　coronary　computed　tomography　angiography　(CCTA)-derived　fractional　flow　reserve　(FFR)　has　recently　attracted　interest.　However,　existing　algorithms　run　at　high　computational　cost.　Therefore,　this　study　proposes　a　fast　calculation　method　of　FFR　for　the　diagnosis　of　ischemia-causing　coronary　stenosis.Methods:　We　combined　CCTA　and　machine　learning　to　develop　a　simplified　single-vessel　coronary　model　for　rapid　calculation　of　FFR.　First,　a　zero-dimensional　model　of　single-vessel　coronary　was　established　based　on　CCTA,　and　microcirculation　resistance　was　determined　through　the　relationship　between　coronary　pressure　and　flow.　In　addition,　a　coronary　stenosis　model　based　on　machine　learning　was　introduced　to　determine　stenosis　resistance.　Computational　FFR　(cFFR)　was　then　obtained　by　combining　the　zero-dimensional　model　and　the　stenosis　model　with　inlet　boundary　conditions　for　resting　(cFFRr)　and　hyperemic　(cFFRh)　aortic　pressure,　respectively.　We　retrospectively　analyzed　75　patients　who　underwent　clinically　invasive　FFR　(iFFR),　and　verified　the　model　accuracy　by　comparison　of　cFFR　with　iFFR.Results:　The　average　computing　time　of　cFFR　was　less　than　2　s.　The　correlations　between　cFFRr　and　cFFRh　with　iFFR　were　r　=　0.89　(p　＜　0.001)　and　r　=　0.90　(p　＜　0.001),　respectively.　Diagnostic　accuracy,　sensitivity,　speci-ficity,　positive　predictive　value,　negative　predictive　value,　positive　likelihood　ratio,　negative　likelihood　ratio　for　cFFRr　and　cFFRh　were　90.7%,　95.0%,　89.1%,　76.0%,　98.0%,　8.7,　0.1　and　92.0%,　95.0%,　90.9%,　79.2%,　98.0%,　10.5,　0.1,　respectively.Conclusions:　The　proposed　model　enables　rapid　prediction　of　cFFR　and　exhibits　high　diagnostic　performance　in　selected　patient　cohorts.　The　model　thus　provides　an　accurate　and　time-efficient　computational　tool　to　detect　ischemia-causing　stenosis　and　assist　with　clinical　decision-making.