There are many factors influencing springback of high strength Ushaped bending parts, such as workpiece size, mechanical properties and load conditions, which make bending springback of highstrength bending parts be difficult to control. The minimum bending radius R and the springback angle α were taken as two objective functions. Firstly, supervised gaussian process latent variable model (SGPLVM) was used for variable selection and dimensionality reduction, the least squares support vector machine (LSSVM) model for Ushaped bending part was constructed. The prediction results of SGPLVM-LSSVM were compared with SVM, FEM prediction results and actual engineering parts to verify the feasibility of the proposed model.