The initial value of the digital image correlation method has a great influence on the calculation efficiency and solution accuracy of the algorithm. For this reason, an algorithm using dense feature matching to obtain the initial value is proposed. The AKZAE operator is used to detect the feature points, the Daisy descriptor is used to describe the feature points, and then the grid motion statistics (GMS) algorithm is used to filter the feature points to obtain the initial value, and finally the initial value into the reverse combined Gaussian in the Newton (IC-GN) method is substituted, the sub-pixel displacement is solved iteratively. Compared with SIFT (Scale Invariant Feature Transform) and SURF (Speeded-Up Robust Features) algorithms, the AKAZE operator improves the accuracy of positioning and has higher computational efficiency. It is a feature point detection algorithm that takes into account both speed and stability. The Daisy descriptor is an efficient dense feature extraction descriptor, which can achieve denser feature extraction compared to other descriptors.