Compared　with　image　sequence,　skeleton　sequence　is　an　ideal　choice　for　human　action　recognition　because　of　no　redundant　information　and　lightweight　data.　Recently,a　tremendous　breakthrough　in　skeleton-based　human　activity　recognition　methods.　By　way　of　illustration,　Spatial-　Temporal　Graph　Convolutional　Networks　(ST-GCN)　creatively　distill　the　information　of　human　joints　into　the　structure　of　a　graph　and　Two-Stream　Adaptive　Graph　Convolutional　Networks　(2S-AGCN)　propose　to　use　the　length　and　orientation　information　of　bones　and　combine　it　with　joint　information　to　predict　in　an　explicit　way.　However　there　are　still　some　issues　exist　in　these　GCN-based　models.　The　model　lacks　long-term　dependency　modeling　capabilities　and　does　not　explore　the　deep　correlation　between　joints　and　bones.　In　this　work,　we　propose　a　temporal　enhanced　multi-stream　graph　convolutional　nerual　networks　(TAMS-GCN)　for　skeleton-based　action　recognition.We　combine　the　temporal　attention　module　with　a　graph　convolutional　neural　network　to　extract　skeletal　information　of　each　frame　and　actively　incorporates　it　into　the　global　features　by　global　pooling　of　all　joints　for　each　frame　to　acquire　attention　at　the　frame　level　of　the　action.　In　addition　we　propose　a　fusion　network　of　joint　and　bone　information　that　implicitly　learns　the　connection　between　joints　and　bones,　increasing　the　compactness　between　joints　and　bones.　We　tested　our　TAMS-GCN　model　on　NTU-RGBD　datasets,　the　model　achieves　excellent　performance　compared　to　the　state-of-the-art.　©　2021　IEEE