Research　on　knowledge　graph　embedding　(KGE)　has　emerged　as　an　active　field　in　which　most　existing　KGE　approaches　mainly　focus　on　static　structural　data　and　ignore　the　influence　of　temporal　variation　involved　in　time　-aware　triples.　In　order　to　deal　with　this　issue,　several　temporal　knowledge　graph　embedding　(TKGE)　approaches　have　been　proposed　to　integrate　temporal　and　structural　information.　However,　these　methods　only　employ　a　uniformly　random　sampling　to　construct　negative　facts.　As　a　consequence,　the　corrupted　samples　are　often　too　simplistic　for　training　an　effective　model.　In　this　paper,　we　propose　a　new　temporal　knowledge　graph　embedding　framework　by　introducing　adversarial　learning　to　further　refine　the　performance　of　traditional　TKGE　models.　In　our　framework,　a　generator　is　utilized　to　construct　high　-quality　plausible　quadruples　and　a　discriminator　learns　to　obtain　the　embeddings　of　entities　and　relations　based　on　both　positive　and　negative　samples.　Meanwhile,　we　also　apply　a　Gumbel-Softmax　relaxation　and　the　Wasserstein　distance　to　prevent　vanishing　gradient　problems　on　discrete　data;　an　inherent　flaw　in　traditional　generative　adversarial　networks.　Through　comprehensive　experimentation　on　temporal　datasets,　the　results　indicate　that　our　proposed　framework　can　attain　significant　improvements　based　on　benchmark　models　and　also　demonstrate　the　effectiveness　and　applicability　of　our　framework.