Compared　to　RGB　video-based　action　recognition,　skeleton-based　action　recognition　algorithm　has　attracted　much　more　attention　due　to　being　more　lightweight,　better　generalization　and　robustness.　The　extraction　of　temporal　and　spatial　features　is　a　crucial　factor　for　skeleton-based　action　recognition.　However,　existing　feature　extraction　methods　suffer　from　two　limitations:　(1)　the　isolated　extraction　of　temporal　and　spatial　feature　cannot　capture　temporal　feature　connections　among　non-adjacent　joints　and　(2)　convolution-limited　perceptual　fields　cannot　capture　global　temporal　features　of　joints　effectively.　In　this　work,　we　propose　a　global　spatio-temporal　synergistic　feature　learning　module　(GSTL),　which　generates　global　spatio-temporal　synergistic　topology　of　joints　by　spatio-temporal　feature　fusion.　By　further　combining　the　GSTL　with　a　temporal　modeling　unit,　we　develop　a　powerful　global　spatio-temporal　synergistic　topology　learning　network　(GSTLN),　and　it　achieves　competitive　performance　with　fewer　parameters　on　three　challenge　datasets:　NTU　RGB　+　D,　NTU　RGB　+　D　120,　and　NW-UCLA.　©　2023