Few-shot　action　recognition　is　the　task　of　predicting　unlabeled　actions　based　on　a　limited　number　of　labeled　actions.　Recently　most　of　few-shot　action　recognition　methods　adopt　GCNs　to　encode　the　skeleton　clips　and　predict　the　label　by　calculating　the　distance　in　spatial-temporal　feature　space.　However,　coarse　encoded　features　will　lead　to　confusion　between　ambiguous　actions　with　similar　spatial　or　temporal　information,　which　results　in　misclassification　among　these　actions.　To　solve　this　problem,　we　propose　a　effective　method　called　Spatial-Temporal　Decoupling　Matching　(STDM)　consisting　of　a　spatial-temporal　decoupling　module　and　a　spatial-temporal　matching　strategy　to　model　the　spatial　and　temporal　information　effectively.　The　spatial-temporal　decoupling　module　maps　confusing　encoded　features　to　spatial　and　temporal　feature　spaces　respectively.　The　spatial-temporal　matching　strategy　simultaneously　recognizes　the　actions　from　the　spatial　and　temporal　perspective.　We　have　conducted　numerous　experiments　on　NTU　RGB+D　120　and　Kinetics　datasets.　Our　method　achieves　comparable　results　and　surpasses　most　of　the　SOTA　methods.　©　2023　IEEE.