Abstract: Nanoindentation experiment has been widely concerned in the field of material mechanics testing due to its advantages of simple sample preparation and wide range of use. A plastic parameter identification method of the Al 2024-T3 aluminum alloy material considering the uncertainty of nanoindentation experiment is established. First of all, the nanoindentation experiment is carried out on the Al 2024-T3 alloy, and the load-displacement curve is obtained. Due to the inhomogeneity of the material, the experimental curve is uncertain. Based on the artificial neural network of superparametric optimization, the relationship between the material performance parameters and indentation response loading curve is established. Based on the interval optimization theory, the uncertainty of the indentation test curve is introduced. Taking the loading curvature of the indentation test curve as the uncertainty quantity, the interval optimization model of the material parameter identification based on the double-layer nested genetic algorithm is proposed, and the inverse problem of the parameter identification is solved. The advantage of this method is that it can take into account the uncertainty of experimental measurement, and the recognition result is more reliable. The validity of the established method has been verified in the identification of the Al 2024-T3 alloy plastic parameters. The identification errors of the yield stress and hardening index are-0.87% and 2.76%, respectively. This recognition method is expected to be used in the detection of mechanical properties of small size specimens.