Graph　embedding　models　are　widely　used　in　knowledge　graph　completion　(KGC)　task.　However,　most　models　are　based　on　the　assumption　that　knowledge　is　completely　certain,　and　this　is　inconsistent　with　real-world　situations.　Although　there　are　multiple　studies　on　uncertain　knowledge　embedding　tasks,　they　often　use　knowledge　confidence　to　learn　embedding　and　cannot　make　full　use　the　structural　and　uncertain　information　of　knowledge.　This　paper　presents　a　new　embedding　model　named　Structural　and　Uncertain　Knowledge　Embedding　(SUKE),　which　comprises　two　components:　an　evaluator　and　a　confidence　generator.　For　unknown　triples,　the　evaluator　learns　the　structural　and　uncertain　information　to　evaluate　its　rationality　and　obtain　a　candidate　set.　The　confidence　generator　then　determines　the　confidence　of　the　candidate　set　to　achieve　KGC.　To　verify　the　effectiveness　of　the　proposed　model,　confidence　prediction,　triple　evaluation,　and　fact　classification　tasks　are　performed　on　three　data　sets.　Experimental　results　show　that　SUKE　performs　better　than　mainstream　embedding　methods.　The　model　proposed　in　this　paper　can　help　advance　the　research　on　the　embedding　of　uncertain　knowledge　graphs.