Exploring　dynamic　patterns　from　complex　and　large-scale　networks　is　a　significant　and　challenging　task　in　graph　analysis.　One　of　the　most　advanced　solutions　is　dynamic　graph　representation　learning,　which　embeds　structural　and　temporal　correlations　into　a　representative　vector　for　each　node　or　subgraph.　Existing　models　have　made　some　successes,　such　as　overcoming　the　problems　of　induction　for　unseen　nodes　and　scalability　for　large-scale　evolving　networks.　However,　these　models　usually　rely　on　crisp　representation　learning　that　is　incapable　of　modeling　feature　fuzziness　and　capturing　uncertainties　in　dynamic　graphs.　While　real-world　dynamic　networks　as　complex　systems　always　contain　non-negligible　but　inestimable　uncertainties　in　node/link　attributes　and　network　topology.　These　uncertainties　may　cause　the　learned　representations　from　crisp　models　hard　to　precisely　reflect　network　evolution.　To　address　the　issues,　we　propose　a　new　dynamic　graph　representation　learning　model,　called　FuzzyDGL,　which　first　incorporates　fuzzy　representation　learning　to　handle　the　uncertainties　in　dynamic　graphs.　Through　combining　CDGRL　with　fuzzy　logic,　the　FuzzyDGL　digests　both　of　their　advantages.　On　the　one　hand,　it　has　flexible　model　scalability　and　brilliant　inductive　capability.　On　the　other　hand,　it　can　model　feature　fuzziness　to　reduce　the　impact　of　uncertainties　in　dynamic　graphs,　improving　the　quality　of　learned　representations.　To　demonstrate　its　effectiveness,　we　conduct　two　important　tasks　of　network　analysis,　including　link　prediction　and　node　classification,　over　eight　real-world　datasets.　The　experimental　results　show　the　strong　competitiveness　and　generalization　of　the　FuzzyDGL　against　a　number　of　baseline　models.