The　quality　of　galvanising　alloying　for　a　continuous　hot-dip　galvanised　steel　strip　subjected　to　the　induction　heating　(IH)　treatment　after　being　delivered　out　of　a　zinc　pot　can　be　effectively　improved.　The　temperature　of　an　IH　outlet　setting　strip　represents　a　key　factor　affecting　the　alloying　quality.　However,　traditional　prediction　models　have　low　accuracy,　which　requires　additional　manual　intervention　and　poses　difficulty　in　improving　the　quality　of　coating　alloying.　To　address　these　challenges,　this　study　proposes　the　vision　transformer-based　algorithm　for　numerical　data　regression,　named　the　vision　transformer　numerical　regression　(ViTNR)　algorithm.　In　addition,　using　the　historical　datasets　of　IH　outlet　strip　temperature　in　the　hot-dip　galvanising　production,　this　study　develops　a　ViTNR　prediction　model　for　IH　outlet　setting　strip　temperature.　The　proposed　ViTNR　model　of　IH　outlet　strip　setting　temperature　is　compared　with　four　typical　deep　learning-based　models,　including　convolution　neural　networks,　and　five　shallow　learning-based　models,　including　the　AdaBoost　model.　The　comparison　results　show　that　the　proposed　ViTNR　model　can　achieve　higher　prediction　accuracy　and　better　generalisation　ability　in　the　IH　outlet　strip　temperature　prediction　application　scenarios　compared　to　the　comparison　models.