3-D　action　recognition　is　referred　to　as　the　classification　of　action　sequences　which　consist　of　3-D　skeleton　joints.　While　many　research　works　are　devoted　to　3-D　action　recognition,　it　mainly　suffers　from　three　problems:　1)　highly　complicated　articulation;　2)　a　great　amount　of　noise;　and　3)　low　implementation　efficiency.　To　tackle　all　these　problems,　we　propose　a　real-time　3-D　action-recognition　framework　by　integrating　the　locally　aggregated　kinematic-guided　skeletonlet　(LAKS)　with　a　supervised　hashing-by-analysis　(SHA)　model.　We　first　define　the　skeletonlet　as　a　few　combinations　of　joint　offsets　grouped　in　terms　of　the　kinematic　principle　and　then　represent　an　action　sequence　using　LAKS,　which　consists　of　a　denoising　phase　and　a　locally　aggregating　phase.　The　denoising　phase　detects　the　noisy　action　data　and　adjusts　it　by　replacing　all　the　features　within　it　with　the　features　of　the　corresponding　previous　frame,　while　the　locally　aggregating　phase　sums　the　difference　between　an　offset　feature　of　the　skeletonlet　and　its　cluster　center　together　over　all　the　offset　features　of　the　sequence.　Finally,　the　SHA　model　combines　sparse　representation　with　a　hashing　model,　aiming　at　promoting　the　recognition　accuracy　while　maintaining　high　efficiency.　Experimental　results　on　MSRAction3D,　UTKinectAction3D,　and　Florence3DAction　datasets　demonstrate　that　the　proposed　method　outperforms　state-of-the-art　methods　in　both　recognition　accuracy　and　implementation　efficiency.