With　the　rapid　development　of　artificial　intelligence　technology,　the　recognition　accuracy　performance　of　traditional　gymnastic　sports　action　recognition　system　can　no　longer　meet　the　needs　of　today＇s　society.　To　address　these　problems,　an　improved　action　recognition　algorithm　combining　Precision　Time　Protocal　(PTP)　and　Convolutional　Neural　Networks　(CNN)　is　proposed,　and　a　human-computer　interaction　gymnastic　action　recognition　system　based　on　PTP-CNN　algorithm　is　constructed.　The　performance　test　of　the　proposed　PTP-CNN　algorithm　was　conducted,　and　it　was　found　that　the　accuracy　of　PTP-CNN　algorithm　was　92.8%　and　the　recall　rate　was　95.2%,　which　was　better　than　the　comparison　algorithm.　The　performance　comparison　experiments　of　the　gymnastic　action　recognition　system　based　on　the　PTP-CNN　algorithm　found　that　the　recognition　accuracy　of　the　PTP-CNN　gymnastic　action　recognition　system　was　96.3%　and　the　running　time　was　3.4s,　which　was　better　than　the　other　comparison　systems.　Comprehensive　results　can　be　found　that　the　research　proposed　PTP-CNN　recognition　algorithm　and　improved　gymnastic　action　recognition　system　can　effectively　improve　the　performance　of　traditional　algorithms　and　models,　which　has　practical　application　value　and　great　application　potential.　©　(2024),　(Science　and　Information　Organization).　All　Rights　Reserved.