Aiming　at　the　problems　of　poor　identification　accuracy　in　small　sample　size　asphalt　pavement　damage　classification　recognition，five　common　types　of　pavement　damage　were　selected.　The　shallow　depth　convolutional　neural　network　model　based　on　VGG　is　designed　as　an　automatic　classification　method　of　asphalt　pavement　damage　image.　Firstly，the　collected　image　samples　are　made　into　data　sets　for　model　training.　Moreover，three　different　batch　sizes　and　two　different　network　layers　are　set　up　for　training，and　the　most　suitable　size　for　the　network　model　is　selected　so　as　to　obtain　the　shallow　VGG.　The　processed　road　image　is　directly　used　as　the　input　of　the　model　for　training，verification　and　testing　of　the　model.　Finally，the　test　result　was　compared　with　SVM　and　the　current　main⁃　stream　deep　convolutional　neural　network.　The　results　show　that　the　classification　accuracy　of　the　training　set，verifi⁃　cation　set　and　a　test　set　of　shallow　VGG　is　close，the　classification　and　recognition　accuracy　rate　of　pavement　damage　image　is　more　than　98%，which　shows　the　good　ability　of　recognition　of　shallow　VGG.　Compared　with　SVM　and　the　current　mainstream　deep　convolutional　neural　network，shallow　VGG　network　model　takes　less　time　and　has　strong　generalization　ability，and　can　capture　more　global　information.　It　can　be　seen　that　the　shallow　VGG　model　has　sig⁃　nificant　advantages　in　the　classification　and　recognition　of　small-scale　images，it　is　more　robust　and　the　results　are　more　accurate　compared　with　other　methods.　©　2023　Hunan　University.　All　rights　reserved.