In order to enhance the anti-noise performance of traditional fuzzy C-means clustering algorithms and preserve the membership similarity between arbitrary pixels and neighboring pixels, a fuzzy C-means color noise image segmentation algorithm with adaptive spatial intensity constraints and Kullback-Leibler(KL) information is proposed. Firstly, the local spatial intensity information is obtained using a fast bilateral filter, which is used to smooth the noisy pixels. Secondly, local weighted average membership is taken as the prior probability, and it is embedded into the objective function by KL information, so as to optimize the membership partition matrix. Finally, the absolute intensity difference between original image and the bilateral filtered image is calculated, and the absolute intensity difference in exponential form is adopted as adaptive weight of the bilateral filtered image, and then its inverse is applied as adaptive weight of original image. When the mixed noise density is 30%, the partition coefficient and partition entropy of the proposed algorithm are 99.66% and 0.58% on the noise synthetic image, and then 98.77% and 2.03% on the real noise image, respectively. Experimental results show that the proposed algorithm has stronger anti-noise performance, higher segmentation accuracy and better stability in comparison with other related algorithms.