Aiming at the accurate segmentation of nuclei in pathological images, a neural network structure which combines the characteristics of fully convolutional network framework and high-resolution network framework is proposed to automatically and accurately segment nuclei. Due to the difference of color distribution in pathological images caused in the staining process, a method based on sparse non-negative matrix decomposition is used to normalize the color distribution of all pathological images. Then the proposed convolutional neural network is used to segment the nuclei accurately with the normalized images as input. By reducing the use of down-sampling operators, the network can make the image information not lose excessively in the forward process, and can expand the size of the local receptive field of neurons in deep layers by using the dilated convolution operators. We test our method on the 2017 MICCAI pathological digital image segmentation data set, and get the average dice score of 0.848. Experiments show that the convolutional neural network fusing fully convolutional network framework and high-resolution network framework can automatically and accurately segment nuclei in pathological images, which can effectively reduce the workload of imaging physicians.