Spatiotemporal　action　detection　relies　on　the　learning　of　video　spatial　and　temporal　information.　The　current　state-of-the-art　convolutional　neural　network-based　action　detectors　have　achieved　remarkable　results　using　2D　CNN　or　3D　CNN　architectures.　However,　due　to　the　complexity　of　the　network　structure　and　spatiotemporal　information　perception,　these　methods　are　usually　used　in　a　non-real-time,　offline　manner.　The　main　challenge　of　spatiotemporal　action　detection　is　to　design　an　effective　detection　network　architecture　and　effectively　perceive　the　fused　spatiotemporal　features.　Aiming　at　the　above　problems,　our　paper　proposes　a　real-time　action　detection　method　based　on　multi-scale　spatiotemporal　feature.　Aiming　at　the　problem　that　only　2D　or　3D　backbone　network　cannot　effectively　model　spatiotemporal　features,　we　extract　spatiotemporal　features　by　multi-branch　feature　extraction　networks　respectively.　For　the　lack　of　descriptiveness　of　single-scale　spatiotemporal　features,　a　multi-scale　spatiotemporal　feature-aware　attention　network　is　proposed　to　learn　long-term　temporal　dependencies　and　spatial　context　information.　And　the　fusion　between　temporal　and　spatial　features　is　guided　by　fusion　attention　to　highlight　more　discriminative　spatiotemporal　feature　representations.　The　proposed　method　achieves　82.59%　and　78.30%　accuracy　on　two　spatiotemporal　action　datasets　UCF101-24　and　JHMDB-21,　respectively　and　reaching　73　frames/s.