The invention provides an improved ceramic material member sequence image segmentation method of a fully convolutional neural network. The method comprises the steps of S10, performing manual marking on an acquired original image, classifying a target and a background by different kinds, obtaining a training label, and representing a label graph of a training sample in an index mode; S20, constructing an improved network model based on the fully convolutional neural network, and performing training; and S30, calculating a loss function and a reverse propagation calculation loss function according to a gradient reducing algorithm, and performing training learning on the network, wherein the learning rate is reduced to one tenth of the original learning rate when verification accuracy increase is stopped. The fully convolutional network is an improved structure based on a convolutional neural network. Based on keeping of good classification performance of the CNN, a spatial position relation between pixel matrixes in better kept, and global characteristic extraction is facilitated. The visual characteristic of an object can be comprehensively studied, and high interference resistance is realized. An objective target can be automatically divided from background, thereby realizing intelligent segmentation.