Stroke　is　a　significant　cause　of　morbidity　and　long-term　disability　globally.　Detection　of　injured　neuron　is　a　prerequisite　for　defining　the　degree　of　focal　ischemic　brain　injury,　which　can　be　used　to　guide　further　therapy.　Here,　we　demonstrate　the　capability　of　two-photon　microscopy　(TPM)　to　label-freely　identify　injured　neurons　on　unstained　thin　section　and　fresh　tissue　of　rat　cerebral　ischemia-reperfusion　model,　revealing　definite　diagnostic　features　compared　with　conventional　staining　images.　Moreover,　a　deep　learning　model　based　on　convolutional　neural　network　is　developed　to　automatically　detect　the　location　of　injured　neurons　on　TPM　images.　We　then　apply　deep　learning-assisted　TPM　to　evaluate　the　ischemic　regions　based　on　tissue　edema,　two-photon　excited　fluorescence　signal　intensity,　as　well　as　neuronal　injury,　presenting　a　novel　manner　for　identifying　the　infarct　core,　peri-infarct　area,　and　remote　area.　These　results　propose　an　automated　and　label-free　method　that　could　provide　supplementary　information　to　augment　the　diagnostic　accuracy,　as　well　as　hold　the　potential　to　be　used　as　an　intravital　diagnostic　tool　for　evaluating　the　effectiveness　of　drug　interventions　and　predicting　potential　therapeutics.