It is crucial to properly design the fixturing layout described by fixturing parameters, such as the fixturing sequence, the placement of clamping force, the locator position, and so on. This is because the clamping deformation of the thin-walled workpiece can influence extremely the machining accuracy and surface quality. Generally speaking, the finite element method can be used to easily obtain the deformation rule of the workpiece caused by one single fixturing parameter. But it is difficult to reveal the relationship between the multiple fixturing parameters and the clamping deformation of workpiece. Therefore, the workable finite element model of multi-fixturing layout is above all established for the thin-walled workpiece. Thus, clamping deformations can be calculated to be the training samples of the neural network. Next, according to the training samples, the prediction model is suggested for obtaining the clamping deformation from multiple fixturing parameters. When the prediction errors are defined as fitness function, the genetic algorithm is developed to search the optimal initial weights and thresholds for the neural network. The optimized neural network has better generalization and prediction ability than the non-optimized one. Ultimately, the embedded optimal model with the objective of minimizing the clamping deformation is presented for a multi-fixturing layout. When the individual fitness of each generation is constructed as a function of the clamping deformations, the genetic algorithm can be skillfully used to solve the embedded optimal model. Moreover, the experiment is conducted to validate the prediction method with good agreement between the predicted results and the experimental data. The above presented "analysis—prediction—control" method of clamping deformation not only improves the calculation efficiency of clamping deformation but also provides a basic theory of fixturing layout design for the thin-walled workpiece.