Abstract: A method was proposed to inverse the temperature dependence of the elastic constant of a material from the dispersion curve using a neural network. The finite element method was employed to calculate the transient temperature field formed by millisecond laser heating of aluminum material. Under the assumption of various temperature dependence of Young's modulus, various dispersion curves of surface waves propagating in the laser heating zone were calculated. The results of the forward calculation were used to train the neural network. After the neural network was trained, by inputting the surface wave dispersion characteristics of the material, the temperature dependence of Young's modulus of materials was inversed. In order to verify the inversion capability of this method, the inversion results under different noises conditions are compared. The inversed results show that this method has good robustness.