The invention relates to a binary neural network stereo vision matching method based on an FPGA. The method comprises the following steps of 1, obtaining periodic input streams of pixels in a binocular matching grayscale image, 2, acquiring an image block from the pixel, 3, inputting the image blocks in the step 2 into a binary neural network with preset weights and parameters to obtain binary feature vectors, 4, performing cost calculation on the feature vector within the maximum search parallax to obtain a matching cost, 5, inputting the cost into semi-global cost aggregation for cost aggregation to obtain aggregated cost, 6, selecting the position with the minimum cost from the aggregated cost as parallax, and 7, carrying out consistency detection and parallax refinement calculation onthe selected parallax to obtain a parallax graph, and outputting parallax values of the pixels one by one according to a period. By means of the binarization method, computing and storage resources ofthe network can be effectively reduced, and therefore the high-precision stereo matching network can be deployed into the FPGA.