A　semantic　ontology-driven　hierarchical　consistency　segmentation　algorithm　was　proposed　to　solve　the　segmentation　inconsistency　of　animation　character　models　because　of　the　changing　of　poses.　The　mapping　between　semantic　labels　and　local　geometric　features　was　extracted　to　form　a　segmentation　ontology.　In　the　process　of　segmentation,　support　vector　machine　(SVM)　and　local　geometric　features　were　used　to　identify　the　semantic　labels,　and　segmentation　was　carried　out　according　to　the　semantic　label　driving　hierarchy　to　ensure　the　consistency　of　the　segmentation　levels　of　animation　character　models.　Poisson　equation　was　used　to　define　contour　lines　for　the　equal　perimeters　of　segmentation　boundary　because　of　the　changing　of　poses.　This　optimization　method　made　the　segmentation　boundary　smooth　and　consistent　under　the　changing　of　poses.　In　the　experiment　part,　all　kinds　of　animation　character　models　under　different　poses　were　verified　and　analyzed,　and　the　consistent　hierarchical　segmentation　effect　was　obtained.　Compared　with　the　existing　methods,　the　proposed　segmentation　ontology　can　solve　the　problem　of　adaptive　selection　of　optimization　parameters　of　different　classes　of　models,　and　improve　the　segmentation　quality.　With　the　continuous　development　of　deep　learning,　the　use　of　image　segmentation　for　animation,　and　human　pose　recognition　will　become　more　and　more　important.