Videos　have　become　widespread　due　to　the　ease　of　obtaining　and　going　share　via　social　platform.　Event　recognition　in　video　has　gained　more　and　more　attention　in　computer　vision.　This　is　a　hard　task　that　requires　extracting　meaningful　spatiotemporal　features　for　event　recognition,　mainly　due　to　complexity　and　diversity　of　video　events.　Many　proposed　networks　learn　spatial　features　and　temporal　separately.　In　this　paper,　we　propose　a　simple,　yet　effective　approach　for　spatio-temporal　features＇　learning:　using　deep　spatial-temporal　neural　networks　based　on　convolution　3D.　The　architecture　is　shown　in　Fig.　1.　The　network　can　capture　the　motion　information　in　multiple　adjacent　frames　and　appearance　information　simultaneously.　Most　of　the　famous　2D　CNN　networks　follow　a　regular　pattern:　the　former　of　convolution　kernel　size　is　bigger　and　the　number　of　channel　in　latter　layers　increase,　such　as　alexnet.　So　we　choose　the　way　that　contacting　two　continuous　convolutional　layers　to　instead　of　a　convolutional　layer　which　its　kernel　size　is　bigger　through　synthetical　consideration.　We　carry　out　experiments　on　KTH　dataset,　and　evaluate　them　using　5-fold　method.　And　this　paper　introduce　two　simple　method　of　increasing　the　amount　of　training　data　and　improving　the　performance　on　both.　Experimental　result　shows　that　our　model　achieve　an　accuracy　of　95.33%　on　KTH　dataset,　we　further　demonstrate　that　our　model　is　a　general　and　effective　architecture　through　compared　to　other　algorithms,　including　hand-crafted　algorithms　and　other　CNNs.　©　2018　IEEE.