Abstract: Abstract: The breeding environment plays an important role in healthy growth and development of the pigs, and it is also a guarantee of the excellent traits. The growth of pig is the integrated result of their own traits and external environmental factors. For different environments, pigs will show different behaviors. It is found that the appropriate growth environment can be reflected by their degree of dispersion in the pens. When the environment is suitable, the pig group will lie down together. When the temperature is low, they will huddle together; and when the environmental temperature is high, the pigs will scatter around. Image segmentation is the important link to analyze pig behavior status based on computer vision technology, and pig image segmentation is an image representation that provides easy-to-understand and analysis for the behavior feature extraction, parameter measurement, image analysis, pattern recognition of individual pig. The accurate and effective image segmentation algorithm is for pig behavioral intelligent analysis, understanding and environmental intelligent decision. The traditional Graph Cut algorithm uses the energy function to compute all the pixels in the image, it takes a lot of time. It has the low segmentation efficiency and it cannot accurately segment specific pig. Aiming at the limitation of traditional Graph Cut algorithm and the characteristic of image itself, on the basis of the interactive watershed algorithm, in this paper, we proposed an interactive image segmentation method based on improved Graph Cut algorithm aiming at better use of interaction information provided by the user and effectively control of the number of super pixels. To some extent, the algorithm could also avoid the problems of over segmentation or under segmentation. The image was divided into regions based on the interactive watershed algorithm, and the foreground and background were specified. Each region block was regarded as the super pixel, the gray scale of super pixel was used as the vertex. Establish edges between adjacent vertices and the new network map was constructed instead of the traditional weighted graph. In order to reconstruct the energy function to complete the effective segmentation of the foreground and background, the maximum flow, and minimization cut algorithm were used to cut the image. Results of experiments showed that the method can effectively segment pig target, it had high segmentation quality and high efficiency. In this paper, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were used as objective evaluation standard. The range of the PSNR was 30, 40 and the range of the SSIM was 0.9, 1 in this method, which was the most accurate segmentation algorithm in the shown segmentation algorithm. It indicated that two evaluation criterions were consistent with the subjective evaluation, and the stability and the reliability of this method were improved. The average time consumed in this algorithm was only 33.7% of in the traditional Graph Cut algorithm. It greatly improved the efficiency of segmentation, and it had highly robust for the segmentation of specific target pigs. The results of this paper can provide technical support for subsequent target identification, detection, tracking and monitoring, evaluation of external environments, such as alarms.