So　as　to　reduce　the　interference　of　noisy　information　and　enhance　the　accuracy　of　car　marker　image　recognition　in　real　life,　a　car　marker　image　recognition　model　based　on　deep　residual　shrinkage　network　is　proposed.　The　model　combines　attention　mechanisms　and　soft　thresholding　function　on　the　basis　of　deep　residual　network,　which　is　used　to　eliminate　noise　and　redundant　information　in　the　data,　thus　reducing　the　interference　of　noise　information　and　improving　the　accuracy　of　image　recognition.　The　experiments　are　conducted　by　adding　Gaussian　noise　and　pretzel　noise　to　the　car　logo　images　shared　by　HFUT-VL　on　Github　to　simulate　the　car　logo　images　captured　under　realistic　conditions,　forming　a　dataset　of　200　images　for　each　car　logo　with　a　total　of　16000　images,　and　then　training　the　deep　residual　shrinkage　network　with　deep　residual　network　and　SENet　algorithm　model　on　the　data.　Then,　the　deep　residual　shrinkage　network　is　compared　with　the　deep　residual　network　and　SENet　algorithm　model　to　train　the　data,　and　some　of　the　car　mark　images　are　tested.　The　results　of　the　experiment　show　that　the　method　has　better　performance　than　other　deep　neural　network　methods.　©　2023　SPIE.