To improve the accuracy and efficiency of the using of static surrogate model for nonlinear structure optimization, the adaptive iteration least square support vector regression(LSSVR)is introduced to research the optimal solution of load path in the T-shape tube hydroforming process. The maximum contact area of the tube and counter punch and the minimum thinning ratio of the thickness are take as the optimization objective, and those the contact area is greater than the simulation value, the maximum thinning ratio of the thickness is smaller than the experimental value and the protrusion height is greater than test values, are select as constraint conditions. The Latin hypercube design is employed to construct the initial support vector regression model, and some extra sampling points are added to reconstruct the support vector regression model to obtain the Pareto optimal solution set during each iteration. Finally the ideal point is used to obtain a compromise solution from the Pareto optimal solution set for the engineers. The contact area of the most satisfactory solution increases 32.42% and the minimum thickness increases 14.97% compared with the experimental results when the protrusion height is not changing worse. The results show that the adaptive iteration LSSVR model can ensure the accuracy and efficiency of the optimization design in a small amount of samples.