Graph　convolutional　networks　(GCNs)　have　been　shown　to　be　effective　in　performing　skeleton-based　action　recognition,　as　graph　topology　has　advantages　in　representing　the　natural　connectivity　of　the　human　bodies.　Nevertheless,　it　is　challenging　to　effectively　model　the　human　joints　spatially　and　temporally,　and　we　are　lacking　attentional　mechanisms　for　critical　temporal　frames　and　important　skeletal　points.　In　this　work,　we　propose　a　novel　GCNs　combined　with　channel-wise　joints　and　temporal　attention　for　skeleton-based　action　recognition.　Our　temporal　attention　module　captures　the　long-term　dependence　of　time　and　then　enhances　the　temporal　semantics　of　key　frames.　In　addition,　we　design　a　channel-wise　attention　module　that　fuses　multi-channel　joint　weights　with　the　topological　map　to　capture　the　attention　of　nodes　at　different　actions　along　the　channel　dimension.　We　propose　to　concatenate　joint　and　bone　together　along　the　channel　dimension　as　the　joint　&　bone　(J　&　B)　modality,　J　&　B　modality　can　extract　hybrid　action　patterns　under　the　coalition　of　channel-wise　joint　attention.　We　prove　the　powerful　spatio-temporal　modeling　capability　of　our　model　on　three　widely　used　dataset,　NTU-RGB　D,　NTU　RGB+D　120　and　Northwestern-UCLA.　Compared　with　leading　GCN-based　methods,　we　achieve　performance　comparable　to　the-state-of-art.