The present invention relates to a method for segmenting cardiac structures in MRI images based on a multi-channel convolutional neural network, which includes collecting heart movie MRI training data of normal people and heart patients, and manually marking the cardiac structures in the training data by experienced doctors as Heart segmentation labeling results, based on the training data to train the heart region extraction model, so that the heart region extraction model can accurately extract the heart region, and train the heart segmentation network according to the heart region extracted from the training data to segment the heart Structure, using the standard segmentation labeling results as a standard to measure the segmentation performance of the constructed heart segmentation network. The present invention uses a heart region extraction model based on a generative confrontation network to extract the heart, which improves the accuracy of the heart region extraction; at the same time, the context information between adjacent layers is used through a multi-channel convolutional neural network, which improves the segmentation accuracy and accuracy. Spend.