Poor　fine　motor　performance　is　an　important　feature　of　children＇s　developmental　coordination　disorder.　To　improve　the　diagnosis　efficiency　of　developmental　coordination　disorder,　computer　vision-based　evaluation　methods　have　become　a　research　hot　topic.　Most　of　the　current　methods　are　based　on　the　evaluation　of　artificial　features,　while　this　paper　proposes　an　automatic　evaluation　method　of　children＇s　fine　movements　based　on　deep　learning.　By　extracting　human　key　points　from　videos,　the　method　extracts　the　time　series　features　of　key　points　and　uses　deep　learning　neural　networks　to　classify　and　predict　children＇s　fine　movements.　The　experimental　results　show　that　the　highest　accuracy　of　this　method　is　81%,　which　provides　an　effective　tool　for　the　auxiliary　diagnosis　of　children＇s　developmental　coordination　disorder.　©　2023　IEEE.