Background　and　objectives:　Instantaneous　wave-free　ratio　(iFR)　is　a　new　invasive　indicator　of　myocardial　ischaemia,　and　its　diagnostic　performance　is　as　good　as　the　＂gold　standard＂　of　myocardial　ischaemia　diagnosis:　fractional　flow　reserve　(FFR).　iFR　can　be　approximated　by　iFR(CT),　which　is　calculated　based　on　noninvasive　coronary　CT　angiography　(CTA)　images　and　computational　fluid　dynamics　(CFD).　However,　the　existing　methods　for　calculating　iFR(CT)　fail　to　accurately　simulate　the　resting　state　of　the　coronary　artery,　resulting　in　low　computational　accuracy.　Furthermore,　the　use　of　CFD　technology　limits　its　computational　efficiency,　making　it　difficult　to　meet　clinical　application　needs.　The　role　of　coronary　microcirculatory　resistance　compensation　suggests　that　microcirculatory　resistance　can　be　adaptively　reduced　to　compensate　for　increases　in　coronary　stenotic　resistance,　thereby　maintaining　stable　myocardial　perfusion　in　the　resting　state.　It　is　therefore　necessary　to　consider　this　compensation　mechanism　to　establish　a　high-fidelity　microcirculation　resistance　model　in　the　resting　state　in　line　with　human　physiology,　and　so　to　achieve　accurate　calculation　of　iFR(CT).　Methods:　In　this　study　we　successfully　collected　clinical　data,　such　as　FFR,　in　205　stenotic　vessels　from　186　patients　with　coronary　heart　disease.　A　neural　network　model　was　established　to　predict　coronary　artery　stenosis　resistance.　Based　on　the　compensation　mechanism　of　coronary　microcirculation　resistance,　an　iterative　solution　algorithm　for　microcirculation　resistance　in　the　resting　state　was　developed.　Combining　the　two　methods,　a　simplified　single-branch　model　combining　coronary　stenosis　and　microcirculation　resistance　was　established,　and　the　noninvasive　and　rapid　numerical　calculation　of　iFR(CT)　was　performed.　Results:　The　results　showed　that　the　mean　squared　error　(MSE)　between　the　pressure　drop　predicted　by　the　neural　network　value　for　the　coronary　artery　stenosis　model　and　the　ground　truth　in　the　test　set　was　0.053　%,　and　correlation　analysis　proved　that　there　was　a　good　correlation　between　them　(r　=　0.99,　p　＜　0.001).　With　reference　to　clinical　diagnosis　of　myocardial　ischaemia　(using　FFR　as　the　gold　standard),　the　diagnostic　accuracy　of　the　iFR(CT)　calculation　model　for　the　205　cases　was　88.29　%　(r　=　0.71,　p　＜　0.001),　and　the　total　calculation　time　was　＜　8　s.　Conclusions:　The　results　of　this　study　demonstrate　the　utility　of　a　simplified　single-branch　model　in　an　iFR(CT)　calculation　method　based　on　haemodynamics　and　deep　learning,　which　is　important　for　noninvasive　and　rapid　diagnosis　of　myocardial　ischaemia.