The　motif　discovery　of　multi-dimensional　time　series　datasets　can　reveal　the　underlying　behavior　of　the　data-generating　mechanism　and　reflect　the　relationship　between　time　series　in　different　dimensions.　The　study　of　motif　discovery　is　of　important　significance　in　environmental　management,　financial　analysis,　healthcare,　and　other　fields.　With　the　growth　of　various　information　acquisition　devices,　the　number　of　multi-dimensional　time　series　datasets　is　rapidly　increasing.　However,　it　is　difficult　to　apply　traditional　multi-dimensional　motif　discovery　methods　to　large-scale　datasets.　This　paper　proposes　a　novel　method　for　motif　discovery　and　analysis　in　large-scale　multi-dimensional　time　series.　It　can　effectively　find　multi-dimensional　motifs　and　the　correlation　among　the　motifs.　The　experimental　results　show　that　the　proposed　method　achieves　better　performance　than　the　related　arts　on　synthetic　and　real　datasets.　It　is　further　validated　on　practical　air　quality　data　and　provides　theoretical　support　for　real　air　pollution　control　in　places　such　as　Beijing.