As　a　fundamental　and　challenging　problem,　non-rigid　structure-from-motion　(NRSfM)　has　attracted　a　large　amount　of　research　interest.　It　is　worth　mentioning　that　NRSfM　has　been　applied　to　dynamic　scene　understanding　and　motion　segmentation.　Especially,　a　motion　segmentation　approach　combining　NRSfM　with　the　subspace　representation　has　been　proposed.　However,　the　current　subspace　representation　for　non-rigid　motions　clustering　do　not　take　into　account　the　inherent　sequential　property,　which　has　been　proved　vital　for　sequential　data　clustering.　Hence　this　paper　proposes　a　novel　framework　to　segment　the　complex　and　non-rigid　motion　via　an　ordered　subspace　representation　method　for　the　reconstructed　3D　data,　where　the　sequential　property　is　properly　formulated　in　the　procedure　of　learning　the　affinity　matrix　for　clustering　with　simultaneously　recovering　the　3D　non-rigid　motion　by　a　monocular　camera　with　2D　point　tracks.　Experiment　results　on　three　public　sequential　action　datasets,　BU-4DFE,　MSR　and　UMPM,　verify　the　benefits　of　method　presented　in　this　paper　for　classical　complex　non-rigid　motion　analysis　and　outperform　state-of-the-art　methods　with　lowest　subspace　clustering　error　(SCE)　rates　and　highest　normalized　mutual　information　(NMI)　in　subspace　clustering　and　motion　segmentation　fields.