For the defects of traditional fuzzy C-means algorithm in image segmentation, a weighted fuzzy C-means clustering algorithm based on dot density function are proposed. Takes the dot density function of image pixels as the weight, and on the basis of inter-class correlation defines a cluster validity function to determine the optimal number of clusters and combines with cluster validity to complete the effective image segmentation. Theoretical analysis and comparative experiments show that the algorithm overcomes the shortcomings of fuzzy means algorithm to some extent and has good classification accuracy in image segmentation.