The　information　collected　on　large-span　prestressed　cables　by　field　sensors　is　susceptible　to　interference,　which　leads　to　inaccurate　collection　of　structural　and　mechanical　parameters　of　large-span　prestressed　cables,　resulting　in　misjudgment　of　structural　safety　performance.　This　paper　proposes　an　intelligent　judgment　method　for　improving　the　sensitivity　of　analyzing　mechanical　parameters　of　prestressed　cables　based　on　digital　twins　(DTs).　The　safety　performance　of　the　structure　was　evaluated　by　analyzing　the　mechanical　parameters.　First,　the　information　during　prestressed　cable　tensioning　is　dynamically　sensed,　thereby　establishing　a　multidimensional　model　of　structural　analysis.　The　virtual　model　is　processed　by　the　model　modification　rule　to　improve　the　robustness　of　the　simulation;　thus,　a　DT　framework　for　the　sensitivity　judgment　of　the　mechanical　parameters　of　the　cable　is　built.　In　the　twin　model,　the　simulation　data　of　the　real　structure　were　extracted.　Probabilistic　analysis　was　performed　using　the　Dempster-Shafer(D-S)　evidence　theory　to　discriminate　the　sensitivity　of　mechanical　parameters　of　each　cable　node　under　the　action　of　external　forces　with　high　accuracy　and　intelligence.　Sensitivity　analysis　provides　a　reliable　basis　for　the　safety　performance　assessment　of　structures.　Taking　the　wheel-spoke-type　cable　truss　as　an　example,　the　application　of　DTs　and　D-S　evidence　theory　to　the　sensitivity　determination　of　cable　mechanical　parameters　under　temperature　fully　verified　that　the　proposed　intelligent　method　can　effectively　evaluate　the　safety　performance　of　the　actual　structure.