The invention provides a one-dimensional convolutional neural network-based improved region growing algorithm for interactive segmentation of a liver CT image. Multiple kinds of information such as gray values, spatial information and different gradient values of pixels are taken into overall consideration through a neural network to serve as growth rules, so the stability of the region growth method is improved, and the segmentation capacity of the algorithm for an edge complex structure is enhanced. The method comprises the following specific steps: firstly, preprocessing an image, extracting slices containing the liver in a CT image sequence set, and converting a CT image into a grayscale image by using a window algorithm; then, carrying out image edge detection, calculating gradient values of a pixel under different edge detection operators to serve as features of the pixel in order to form a pixel feature vector; constructing a network model, extracting a training data set, and training the network model; and finally, performing segmentation, using the trained convolutional neural network model as a growth criterion of a region growth algorithm, using a mouse to click a liverregion to generate an initial segmentation result, and using a morphological method to fill holes to obtain a final result.