The invention provides a liver tumor segmentation method and device based on a CT (Computed Tomography) image. The method comprises the following steps: performing Gaussian denoising on CT image data of a liver, converting the denoised CT image data into standardized data of which a gray average is 0 and a variance is 1, and performing down-sampling operation; extracting a lesion slice and a normal tissue slice from a gold standard image of the CT image of the liver, and classifying the lesion slice and the normal tissue slice into a positive sample and a negative sample; constructing a multi-level depth convolutional neural network, training a model through a stochastic gradient descent to obtain a network model, and acquiring a coarse segmentation binary image of a tumor and a pixel-classification probability image through a classifier; performing morphological erosion operation on the coarse segmentation binary image of the tumor to obtain a foreground image needed by graph cut, performing subtraction operation on the binary image of a liver and the coarse segmentation binary image of the tumor, and performing the morphological erosion operation to obtain a background image corresponding to normal tissues of the liver; and constructing an undirected graph, and obtaining a finial segmentation region of the tumor through a graph cut optimization algorithm.