Human　action　recognition　based　on　3D　skeleton　joints　is　an　important　yet　challenging　task.　While　many　research　work　are　devoted　to　3D　action　recognition,　they　mainly　suffer　from　two　problems:　complex　model　representation　and　low　implementation　efficiency.　To　tackle　these　problems,　we　propose　an　effective　and　efficient　framework　for　3D　action　recognition　using　a　global-and-local　histogram　representation　model.　Our　method　consists　of　a　global-and-local　featuring　phase,　a　saturation　based　histogram　representation　phase,　and　a　classification　phase.　The　global-and-local　featuring　phase　captures　the　global　feature　and　local　feature　of　each　action　sequence　using　the　joint　displacement　between　the　current　frame　and　the　first　frame,　and　the　joint　displacement　between　pairwise　fixed-skip　frames,　respectively.　The　saturation　based　histogram　representation　phase　captures　the　histogram　representation　of　each　joint　considering　the　motion　independence　of　joints　and　saturation　of　each　histogram　bin.　The　classification　phase　measures　the　distance　of　each　joint　histogram-to-class.　Besides,　we　produce　a　novel　action　dataset　called　BJUT　Kinect　dataset,　which　consists　of　multi-period　motion　clips　and　intra-class　variations.　We　compare　our　method　with　many　state-of-the-art　methods　on　BJUT　Kinect　dataset,　UCF　Kinect　dataset,　Florence　3D　action　dataset,　MSR-Action3D　dataset,　and　NTU　RGB+D　Dataset.　The　results　show　that　our　method　achieves　both　higher　accuracy　and　efficiency　for　3D　action　recognition.