In recent years, spectral clustering algorithms have been widely used in the field of pattern recognition and computer vision, and the construction of the similarity matrix is the key issue of spectral clustering algorithms. Due to the high computational complexity, it is hard to apply spectral clustering algorithms to large scale image segmentation. Aiming at this problem, an interval fuzzy spectral clustering algorithm for image segmentation is proposed. The method firstly uses the grayscale histogram and the interval fuzzy theory to obtain the interval fuzzy membership degree between image grayscales, then uses this membership degree to construct the grayscalebased interval fuzzy similarity measure. Finally, the similarity measure is used to construct the similarity matrix and the image are grouped by normalized cut criterion so as to obtain the final image segmentation results. Due to the introduction of interval fuzzy theory, the segmentation performance of traditional spectral clustering algorithms is improved, and the comparative experiments also show that the algorithm greatly improves segmentation effect and computational complexity.