Affected by imaging distance， illumination， surface features， environment and other factors， objects of the same category in remote sensing images may have certain differences， while objects of different categories instead show similar visual features， which leads to uncertainty in segmentation， that is intra-class heterogeneity and inter-class ambiguity. To solve these problems， a Fuzzy Multiscale Convolutional Neural Network （FMCNet） was proposed for remote sensing image segmentation. By extracting receptive fields of different scales， sizes and aspect ratios， the detailed information in remote sensing objects was fully represented， and fuzzy logic was used to effectively express the relationship between pixels and their adjacent pixels， thus overcoming the uncertainty problem in remote sensing image segmentation. Experimental results show that the Overall Accuracy （OA） of FMCNet on ISPR Vaihingen and Potsdam datasets is 85.3% and 86.3% respectively， outperforming the existing state-of-the-art semantic segmentation methods.