To address the challenges associated with inaccurate target edge segmentation, motion speed, and motion direction, this paper introduces an optical flow estimation network that leverages local correlation information between video frames. Initially, the network employs a feature encoder to extract encoding features from the image and capture contextual information through a context network. Subsequently, the feature size is reduced through downsampling to enhance computational efficiency. Given the minute displacement of the optical flow image across consecutive frames, a partition-based visual similarity computation method is proposed to construct a more refined 4D correlation volume. Residual filters and similar convolution blocks are utilized for processing the correlation volume and optical flow information, respectively, ensuring the preservation of local small displacement details. The research results show that the optical flow estimation network based on the local correlation information between video frames has achieved optimizations of 8.0% and 5.7% respectively in the optical flow estimation evaluation metric (endpoint error, EPE). This significantly improves the accuracy of optical flow estimation and effectively alleviates the problem of inaccurate optical flow information extraction in complex scenarios. The research conclusions provide references for fields such as autonomous driving and intelligent security.