Nowadays,　cardiovascular　and　cerebrovascular　diseases　caused　by　venous　thromboembolism　become　main　causes　of　mortality　around　the　world.　The　current　thrombolytic　strategies　in　clinics　are　confined　primarily　due　to　poor　penetration　of　nanoplatforms,　limited　thrombolytic　efficiency,　and　extremely-low　imaging　accuracy.　Herein,　a　novel　nanomotor　(NM)　is　engineered　by　combining　iron　oxide/perfluorohexane　(PFH)/urokinase　(UK)　into　liposome　nanovesicle,　which　exhibits　near-infrared/ultrasound　(NIR/US)　triggered　transformation,　achieves　non-invasive　vein　thrombolysis,　and　realizes　multimodal　imaging　diagnosis　altogether.　Interestingly,　a　three-step　propelled　cascade　thrombolytic　therapy　is　revealed　from　such　intelligent　NM.　First,　the　NM　is　effectively　herded　at　the　thrombus　site　under　guidance　of　a　magnetic　field.　Afterwards,　stimulations　of　NIR/US　propel　phase　transition　of　PFH,　which　intensifies　penetration　of　the　NM　toward　deep　thrombus　dependent　on　cavitation　effect.　Ultimately,　UK　is　released　from　the　collapsed　NM　and　achieves　pharmaceutical　thrombolysis　in　a　synergistic　way.　After　an　intravenous　injection　of　NM　in　vivo,　the　whole　thrombolytic　process　is　monitored　in　real-time　through　multimodal　photoacoustic,　ultrasonic,　and　color　Doppler　ultrasonic　imagings.　Overall,　such　advanced　nanoplatform　provides　a　brand-new　strategy　for　time-critical　vein　thrombolytic　therapy　through　efficient　thrombolysis　and　multimodal　imaging　diagnosis.　A　kind　of　iron　oxide/perfluorohexane/urokinase-based　liposome　nanomotor　with　near-infrared/ultrasound　triggered　transformation　ability　is　elaborately-engineered　for　achieving　relatively　non-invasive　vein　thrombolysis　and　multimodal　imaging　diagnosis.　Such　polystage-propelled　thrombolytic　cascade　strategy　greatly　improves　thrombolytic　efficiency　through　magnetic　field　navigation,　cavitation　effect　of　perfluorohexane　phase　transition,　and　synergistic　hyperthermia　or　pharmaceutical　thrombolysis,　presenting　excellent　prospects　of　clinical　translations　for　time-critical　vein　thrombolytic　therapy.image