To improve the accuracy of edge detection and ensure the efficiency and effect of image segmentation, an ant colony image enhancement algorithm based on fuzzy clustering is proposed on the basis of ant colony algorithm. The algorithm uses component grayscale value, grayscale gradient value and domain eigenvalue to extract image features, then uses fuzzy clustering to specify the clustering center to improve the convergence speed, then uses the ant colony algorithm to realize the image edge detection, in the process of detection, using the path selection strategy to search the ant colony in order to improve the search efficiency, According to the pheromone update strategy, the optimal path information exchange is realized in order to achieve the purpose of edge point extraction and retrieval, and finally the processed grayscale edge graph coincides with the original picture to realize the effect of image enhancement. Experimental results show that the improved algorithm improves the retrieval time compared with the traditional ant colony algorithm by 20.7%, improves the accuracy by 14.8%, and the texture is clearer in the aspect of image segmentation.