A　Motion　Data　Automatic　Segmentation　using　a　Probabilistic/Kernel　principal　component　analysis　(P/KPCA)　method　is　proposed.　This　approach　utilizes　Kernel　principal　component　analysis　(KPCA)　to　construct　a　kernel　function　while　using　Probabilistic　principal　component　analysis　(PPCA)　to　reduce　motion　noise.　Formulate　the　feature　function　to　obtain　the　derivative　of　projection　error,　and　detect　the　segmentation　point　of　data　through　analyzing　the　change　of　geometric　features　to　realize　the　automatic　segmentation.　It　is　indicated　in　the　experiment　that　the　motion　capture　technique　has　certain　feasibility.　The　paper　presents　the　automatic　segmentation　approach　of　the　motion　capture　data,　in　which　the　motion　data　is　automatic　segmented　through　KPCA　combined　with　PPCA　to　reduce　the　dimension　and　project　the　56　dimensional　data　in　2　dimensional　space;　formulate　the　feature　function　to　obtain　the　derivative　of　projection　error,　and　detect　the　segmentation　point　of　data　through　analyzing　the　change　of　geometric　features　to　realize　the　automatic　segmentation.　It　is　indicated　in　the　experiment　that　the　motion　capture　technique　has　certain　feasibility.　©　2016　-　IOS　Press　and　the　authors.　All　rights　reserved.