In order to reduce the influence of changes such as flotation bubble merging and breaking on the foam surface flow feature extraction, a foam surface flow rate detection method based on infrared target segmentation and improved SURF matching in NSST domain is proposed. First, two adjacent froth infrared images are decomposed through NSST, and boundary, brightness, and saliency constraint terms of the graph cut energy function are constructed in the multi-scale domain to realize the segmentation of the merged and broken bubbles. Then, SURF feature point detection are performed on the segmented background region. The main direction of the feature point is determined by statistical the scale correlation coefficients in the sector area, and the multi-directional high-frequency coefficients in the neighborhood of the feature point are used to construct the feature descriptors. Finally, feature points are matched for two adjacent froth infrared images, and the magnitude, direction, acceleration and disorder of foam flow velocity are calculated based on the matching results. The experimental results show that the method in this paper can effectively segment the merged and broken bubbles with high segmentation accuracy, improve the matching accuracy of SURF algorithm, reduce the impact of the bubbles merging and breaking on the flow velocity detection. Compared with the existing methods, the method in this paper improve the detection accuracy and efficiency, which can accurately characterize the flow characteristics of the foam surface under different working conditions and lay the foundation for the subsequent working condition identification.