Effective　feature　learning　is　one　of　the　prime　components　for　human　action　recognition　algorithm.　Three-dimensional　convolutional　neural　network　(3D　CNN)　can　directly　extract　spatio-temporal　features,　however　it　is　insufficient　to　capture　the　most　discriminative　part　of　the　action　video.　The　redundant　spatial　regions　within　and　between　temporal　frames　would　weak　the　descriptive　ability　of　the　3D　CNN　model.　To　address　this　problem,　we　propose　a　lightweight　spatio-temporal　attention　module　(ST-AM),　composed　of　spatial　attention　module　(SAM)　and　temporal　attention　module　(TAM).　SAM　and　TAM　can　effectively　encode　the　semantic　spatial　areas　and　suppress　the　redundant　temporal　frames　to　reduce　misclassification.　The　proposed　SAM　and　TAM　have　complementary　effects　and　can　be　easily　embedded　into　the　existing　3D　CNN　action　recognition　model.　Experiment　on　UCF-101　and　HMDB-51　datasets　shows　that　the　ST-AM　embedded　model　achieves　impressive　performance　on　action　recognition　task.　©　2022　ACM.