Aiming　at　the　needs　for　auto-driving　vehicles　to　recognize　continuous　traffic　police　gestures,　a　graph　convolutional　traffic　police　gesture　recognizer　with　the　height　layering　partitioning　strategy　is　proposed.　Firstly,　according　to　the　natural,　assist,　self-connection　spatial　relationships　and　temporal　associations　between　human　parts,　a　spatial-temporal　traffic　gesture　model　is　established.　Secondly,　the　spatial-temporal　convolutional　network　is　introduced,　and　the　height　layering　partitioning　strategy　is　proposed,　which　uses　the　relative　height　differences　of　parts　as　labels　and　breaks　the　limitations　of　existing　partitioning　strategies　on　graph　structure.　Finally,　the　spatial　mean　layer　output　structure　which　retains　the　length　of　the　temporal　dimension　is　designed　to　adapt　the　many-to-many　prediction　pattern　for　recognizing　continuous　traffic　police　gestures.　The　experiments　show　that　the　proposed　method　significantly　improves　the　recognizing　performance,　the　confusion　rate　between　gestures　is　0.1%　and　the　Jaccard　index　surpasses　comparison　methods.　©　2022　Institute　of　Computing　Technology.　All　rights　reserved.