Aiming at the problems of unclear target contour segmentation and poor granularity in existing optical flow estimation methods, an optical flow estimation via fusing sequence image intensity correlation information is proposed. First, The coding features and contextual features of the images are extracted by the feature encoder and the global encoder, respectively, and the feature sizes are reduced by downsampling processing. Then, before constructing 4D correlation volume, the input two consecutive frames of feature maps are divided into regions to calculate dense visual similarity in the form of strong and weak correlation to build a more refined 4D correlation volume. Finally, in the iterative update stage, the residual convolution filter and the fine-grained module are proposed to be applied to process the correlation volume and optical flow transmission, respectively, which allows to retain more local small displacement information before fusing the correlation volume information and optical flow information. In comparison with other methods on the KITTI-2015 and MPI-Sintel, the optical flow estimation evaluation metric (Endpoint error, EPE) is improved by 8.2% and 6.15%, respectively. The network model given in this paper can better improve the accuracy of optical flow estimation and effectively solve the problems of the optical flow prediction field being over smooth, lacking of fine granularity and ignoring of small object motion.