Temporal　knowledge　graph　(TKG)　embedding　has　received　increasing　attention　in　the　academia.　However,　most　existing　methods　are　extensions　of　traditional　translation　models.　Due　to　their　intrinsic　limitations,　it　is　often　difficult　for　such　methods　to　effectively　model　essential　characteristics　of　TKG,　namely　three　basic　relation　patterns　including　symmetry/antisymmetry,　inversion,　and　composition.　In　this　paper,　a　new　3-Dimensional　Rotation　Temporal　Embedding　(3DRTE)　method　is　proposed.　Firstly,　we　selectively　fuse　temporal　and　relational　features　of　fact　triples　by　taking　advantages　of　self-attention　mechanism　in　processing　sequential　information.　Then,　entities　are　modelled　as　points　in　three-dimensional　space,　and　the　relations　are　interpreted　as　two　isoclinic　rotations　between　entities　with　Quaternion.　Experimental　results　on　several　public　datasets　show　that　our　method　obtains　state-of-the-art　results.