In　this　paper,　the　author　collects　corresponding　pictures　with　Chinese　knots　on　the　Internet　and　unifies　their　format　as.jpg.　After　using　Colabeler　to　label　the　Chinese　knot　in　each　picture,　import　the　xml　file　and　the　xml　file　in　pascal-voc　format　respectively.　Put　the　prepared　files　into　the　corresponding　folders　in　the　yolo-master　project　to　complete　the　production　of　the　data　set.　After　ensuring　that　the　running　environment　of　yolo-master　is　normal,　adjust　the　corresponding　parameters,　run　the　train.py　file　and　use　this　data　set　to　train　the　model　for　detecting　Chinese　knots.　The　authors　then　test　the　model　on　a　dataset　of　images　mixed　with　other　objects　and　obtain　corresponding　detection　results.　Finally,　the　authors　analyze　the　results　using　metrics　such　as　precision,　recall,　and　F1　score.　Furthermore,　we　discuss　the　novelty　and　future　development　potential　of　the　method　for　object　detection　and　recognition　in　detecting　classical　Chinese　artworks.　©　2023　SPIE.