The inherent flexibility of large thin-walled structures results in complex assembly deviations influenced by manufacturing errors and deformation during assembly, which can significantly impact the final product quality. Current methodologies struggle to balance precision and efficiency when addressing flexible deviations, thus falling short in providing the precise and rapid predictions necessary for large thin-walled structures facing multifaceted deviations. To bridge this gap, this paper introduces an innovative predictive approach based on the Conditional Generative Adversarial Network (cGAN) framework, which integrates advanced image processing to meticulously examine the assembly of thin-walled curved components. The study begins with an in-depth analysis of the principal sources of deviation during assembly, followed by the development of an innovative image fusion strategy designed to synthesize assembly deviation data. Focusing on hyperboloid panel docking as a case study, this research constructs a tailored deviation dataset and facilitates the training of the deviation model. Empirical testing and experimental results indicate that the cGAN deviation prediction model is adept at performing flexible deviation prediction tasks with limited datasets, offering a unique blend of efficiency and precision. The predictive methodology presented here enhances the capability of flexible deformation deviation analysis for large thin-walled structures, surpassing the traditional influence coefficient method in predictive accuracy, offering substantial promise for deviation analysis and tolerance optimization within the field of thin-walled structures.