Prefabricated　construction　hoisting　has　one　of　the　highest　rates　of　fatalities　and　injuries　compared　to　other　construction　processes,　despite　technological　advancements　and　implementations　of　safety　initiatives.　Current　safety　risk　management　frameworks　lack　tools　that　are　able　to　process　in-situ　data　efficiently　and　predict　risk　in　advance,　which　makes　it　difficult　to　guarantee　the　safety　of　hoisting.　Thus,　this　article　proposed　an　intelligent　safety　risk　prediction　framework　of　prefabricated　construction　hoisting.　It　can　predict　the　hoisting　risk　in　real-time　and　investigate　the　spatial-temporal　evolution　law　of　the　risk.　Firstly,　the　multi-dimensional　and　multi-scale　Digital　Twin　model　is　built　by　collecting　the　hoisting　information.　Secondly,　a　Digital　Twin-Support　Vector　Machine　(DT-SVM)　algorithm　is　proposed　to　process　the　data　stored　in　the　virtual　model　and　collected　on　the　site.　A　case　study　of　a　prefabricated　construction　project　reveals　its　prediction　function　and　deduces　the　spatial-temporal　evolution　law　of　hoisting　risk.　The　proposed　method　has　made　advancements　in　improving　the　safety　management　level　of　prefabricated　hoisting.　Moreover,　the　proposed　method　is　able　to　identify　the　deficiencies　regarding　digital-twin-level　control　methods,　which　can　be　improved　towards　automatic　controls　in　future　studies.