The　formation　and　propagation　of　microcracks　are　critical　stages　leading　to　fatigue　failure.　Traditional　methods　for　microcrack　analysis　essentially　rely　on　manual　detection,　which　poses　challenges　in　accuracy　and　efficiency.　The　present　investigation　deals　with　a　novel　and　highly　effective　strategy　for　detecting,　classifying,　and　analyzing　fatigue　microcracks　in　Ti-6Al-4　V　(TC4)　titanium　alloy　with　artificial　intelligence　(AI)　in　the　context　of　deep　learning.　By　integrating　in-situ　scanning　electron　microscope　(SEM)　images　and　convolutional　neural　network　(CNN)　algorithm,　we　propose　a　PGI-CrackNet　model　that　is　able　to　detect　microcracks　of　length　around　15　mu　m　and　thereby　effectively　outperform　the　detection　capabilities　of　traditional　models.　Based　on　fracture　mechanics　models,　the　proposed　model　is　capable　of　automatically　identifying　the　main　stages　(i.e.,　initial　crack,　type　I　crack,　type　II　crack,　and　break)　in　the　formation　of　microcracks.　Simultaneously,　the　proposed　model　bridges　the　gap　between　the　AI-based　image　analysis　and　the　physical　crack　propagation　models,　enabling　the　extraction　of　key　information　such　as　microcrack　length　and　width,　and　further　supporting　the　analysis　of　fatigue　crack　growth　rates　associated　with　various　microcrack　stages.　The　model　could　discover　that　in　the　initiation　stage,　the　crack　of　TC4　titanium　alloy　grows　at　a　fairly　slow　rate　(similar　to　3.6　mu　m/cycle)　and　occupies　most　of　the　crack　life　cycle.　After　the　initiation　stage,　the　crack　first　propagates　as　the　type　I　cracks　with　a　significantly　faster　crack　growth　rate　(similar　to　50　mu　m/cycle).　Then,　the　type　II　crack　occurs　with　a　substantially　reduced　growth　rate　(similar　to　25　mu　m/cycle).　In　the　final　stage,　as　the　microcrack　reaches　a　critical　size,　the　growth　rate　increases　sharply,　leading　to　break.　In　summary,　this　improved　PGI-CrackNet-based　model　enables　more　accurate　tracking　of　crack　growth　over　the　fatigue　life　of　materials　and　better　classification　of　crack　types　based　on　their　propagation　mechanisms,　making　it　highly　suitable　for　early　warning　applications　of　material　failure.