The selection of initial values in digital image correlation (DIC) method has a great influence on the search efficiency of image subpixel displacement and the algorithmic convergence speed. A new digital image correlation method based on KAZE feature matching, namely KAZE algorithm, is proposed in this paper. Compared with the traditional feature detection algorithm, the KAZE algorithm can eliminate noise and extract feature points by establishing nonlinear scale space, which can effectively avoid boundary blurring and detail loss. The coordinates of feature points before and after image deformation are matched by the KAZE algorithm, and the initial deformation parameters are estimated by affine transformation; the inverse combined Gaussian Newton method is used to iteratively optimize the estimated initial values. The results show that this method has higher search efficiency than traditional methods without precision loss.