Effectively　modeling　spatio-temporal　information　in　the　videos　is　the　key　to　improving　the　performance　of　action　recognition.　In　this　work,　we　propose　3D　residual　networks　with　channel　and　spatial　attention　modules　for　action　recognition.　The　proposed　network　architecture　can　directly　extract　spatiotemporal　features.　Channel　attention　module　and　spatial　attention　module　can　effectively　assist　the　network　to　learn　what　and　where　to　emphasize　or　suppress,　at　virtually　negligible　increase　in　computation　cost.　Specifically,　we　sequentially　add　channel　attention　module　and　spatial　attention　module　to　each　slice　tensor　of　the　intermediate　feature　map　to　form　channel　and　spatial　attention　maps.　Then　the　attention　maps　are　multiplied　to　the　input　feature　map　to　reweight　important　features.　We　validate　our　network　through　extensive　experiments　and　visualization　method　on　the　datasets　of　HMDB-51　and　UCF-101.