Although　video　action　recognition　has　achieved　great　progress　in　recent　years,　it　is　still　a　challenging　task　due　to　the　huge　computational　complexity.　Designing　a　lightweight　network　is　a　feasible　solution,　but　it　may　reduce　the　spatio-temporal　information　modeling　capability.　In　this　paper,　we　propose　a　novel　novel　spatio-temporal　collaborative　convolution　(denote　as　＇STC-Conv＇),　which　can　efficiently　encode　spatio-temporal　information.　STC-Conv　collaboratively　learn　spatial　and　temporal　feature　in　one　convolution　filter　kernel.　In　short,　temporal　convolution　and　spatial　convolution　are　integrated　in　the　one　STC　convolution　kernel,　which　can　effectively　reduce　the　model　complexity　and　improve　the　computational　efficiency.　STC-Conv　is　a　universal　convolution,　which　can　be　applied　to　the　existing　2D　CNNs,　such　as　ResNet,　DenseNet.　The　experimental　results　on　the　temporal-related　dataset　Something　Something　V1　prove　the　superiority　of　our　method.　Noticeably,　STC-Conv　enjoys　more　excellent　performance　than　3D　CNNs　at　even　lower　computation　cost　than　standard　2D　CNNs.　©　2020　IEEE.