Dust, pollutant and the aerosol particles in the air bring significant challenge to the atmospheric prediction, and the segmentation of millimeter-wave radar cloud image has become a key to deal with the problem. This paper presents superpixel analysis based cloud image segmentation with fully convolutional networks (FCN) and convolutional neural networks (CNN), named FCN-CNN. Firstly, the superpixel analysis is performed to cluster the neighborhood of each pixel in the cloud image. Then the cloud image is given to the FCN with different steps, such as FCN 32s and FCN 8s. The "non-cloud" area in the FCN 32s result must be a part of the "non-cloud" area in the cloud image. Meanwhile, the "cloud" area in the FCN 8s result must be a part of the "cloud" area in the cloud image. The remaining uncertain region of the cloud image needs to be further estimated by CNN. For efficiency, it is necessary to select several key pixels in the superpixel to represent the characteristics of the superpixel region. The selected key pixels are classified by CNN as "cloud" or "non-cloud". The experimental results illustrate that while the accuracy of FCN-CNN is almost equivalent to MR-CNN and SP-CNN, the speed is 880 times higher than MR-CNN, and 1.657 times higher than SP-CNN.