During the block assembly, when large curved thin plates (such as outer plates) are placed on the jigs, they undergo deformation due to the force of gravity This deformation affects the accuracy and quality of the block assembly in shipbuilding. In order to predict the deformation of these large curved thin plates within a given jig layout, this paper introduces a Transformer-based surrogate model with two-stage Latin hypercube sampling (TSM-TLHS). Primarily, compared to traditional approaches, the two-stage Latin hypercube sampling method enables direct sampling of irregularly shaped thin plates. Simultaneously, a Transformer-based surrogate model incorporating multi-head attention modules and positional encoding is used to comprehensively consider the impact of jig positions and corresponding node displacements on thin plate deformation. Real-case results demonstrate that the prediction error of this TSM-TLHS method is only 0.061mm, meeting the on-site assembly precision requirements for predicting plate deformation. This facilitates timely anti-deformation compensation by block in shipyards, ensuring assembly quality.