With　the　growth　of　image　classification　models　and　the　development　of　deep　learning,　the　level　of　computer　image　classification　has　basically　surpassed　that　of　humans,　but　the　level　of　fine-grained　computer　image　classification　is　still　weaker　than　that　of　humans,　so　fine-grained　image　classification　is　currently　given　a　lot　of　attention.　This　paper　carries　out　visualized　analysis　for　the　fine-grained　image　classification　in　terms　of　cooperation,　scientific　research,　research　trend　and　other　aspects　by　CiteSpace　software,　and　also　points　out　that　fine-grained　image　classification　relies　on　CNN　while　looking　into　Transformer.　However,　fine-grained　image　classification　has　the　problems　of　model　reliance　on　prior　knowledge　(CNN),　lack　of　sufficient　data　(Transformer),　and　lack　of　local　relevance　(CNN/Transformer),　which　can　be　solved　by　reducing　the　complexity　of　the　model,　transfer　learning　data　augmentation,　combination　of　CNN　with　Transformer,　etc.　On　this　basis,　this　paper　indicates　that　Swin-Transformer　has　good　generalization,　hierarchy　and　translation　invariance,　and　will　become　the　main　research　direction　for　fine-grained　image　classification　in　the　future.　©　2022　SPIE.