Aluminum alloy multi-cell thin-walled tubes have better mechanical properties in energy absorption than ordinary square tubes in axial compression conditions， with a wide range of application prospects in automotive， aviation， military equipment， and other industries. To study the anisotropic characteristics of extruded 6061-T6 aluminum alloy material， uniaxial tensile mechanical properties tests are conducted on the sheet along the extrusion direction of 0°， 45°， and 90°. The corresponding stress-strain curves and anisotropic characteristic parameters are obtained， and the material constitutive model is established based on the yield criterion of anisotropic hardening behavior. Tubes with different cross-sectional configurations shaped as the Chinese characters of mouth， day and eye are designed and quasi-static crushing tests are conducted. By analyzing the deformation crushing force curve， it is shown that the thin-walled structure of the triple-cell alloy has superior crash resistance performance. In order to further obtain the optimal design parameters of the triple-shaped tube， considering the uncertain effect of material parameter fluctuations such as Poisson's ratio and elastic modulus on the structural impact resistance， the multi-cell aluminum alloy thin-walled tube impact resistance interval uncertainty optimization model is established. The interval possibility degree method is used to transform it into a deterministic problem. By combining the Artificial Neural Networks （ANNs） model with the Intergeneration Projection Genetic Algorithm （IP-GA） method， a double-layer nested optimization is performed on this problem to analyze the impact of different likelihood levels on uncertainty optimization results， providing guidance for the selection of different reliability optimization design.