To construct a high qualitative disparity space image in stereo vision, a fast global optimal matching algorithm based on Gibbs Random Field(GRF) model was proposed. In this algorithm, the relationship between a space point and its neighborhood was described by using the GRF, and an improved Graph Cut method was used to calculate the matching relationship of the neighborhoods and to obtain the density disparity space image of a scene. Firstly, a set of matching points with distinct matching relationship was calculated, and named them as support points. Then, these support points were taken for the center and their neighborhood spaces were expanded. The improved Graph Cuts algorithm was used to match the expanded neighborhood spaces, and then set the neighborhood points that meet matching degree as new support points. Repeating the above steps and extending progressively, until the expansion of the neighborhood covered the entire the scene image and the density disparity map of the image pair was obtain finally. Experimental results show that this method has good speed consistency on the disparity map of the different scenes, and the matching time is about 0.8-1.2 s. For reflecting the practical value of the algorithm, the proposed algorithm was adopted to construct the disparity map of a real scene on the binocular vision test bed Smart Eye Ⅱ, and good reconstruction results were obtained.