Aiming at the problem that the complex and diverse background in insulator images makes it difficult to extract insulator regions in practice, proposes an improved PCM clustering algorithm to segment the insulator image. To improve the algorithm from two aspects,firstly, by defining the local correlation factor, introducing the spatial local information of the image enhances the anti-jamming ability and improves the segmentation accuracy, secondly, adding the repulsive term of class center to the loss function alleviates the central point overlapping problem of traditional PCM. In the experiment, using artificial data and images of insulator with complex background to compare the proposed algorithm with FCM, PCM, K-means, KFCM and IFCM clustering algorithm. The results show that, the improved PCM has stronger anti-interference ability and higher clustering accuracy, which has better segmentation performance for insulator images than other contrast methods, and the average segmentation error is 0. 153.