In order to effectively prevent and treat heart-based diseases, the study of precise segmentation of heart parts is particularly important. The heart is divided into four parts: the left and right ventricles and the left and right atria, and the left main trunk is more important, thus the left ventricular muscle (LV-MYO), which is located in the middle part of the heart, has become the object of many researches. Deep learning medical image segmentation methods become the main means of image analysis and processing at present, but the deep learning methods based on traditional convolutional neural network (CNN) are not suitable for segmenting organs with few labels and few samples like the heart, while the meta-learning methods are able to solve the above problems and achieve better results in the direction of heart segmentation. Since the LV-MYO is wrapped in the left ventricular blood pool (LV-BP), this paper proposes a new model for heart segmentation: principle component analysis network (PCA-Net). Specifically, we redesign the coding structure of Q-Net and make improvements in threshold extraction. Experimental results confirm that PCA-Net effectively improves the accuracy of segmenting LV-MYO and LV-BP sites on the CMR dataset, and is validated on another publicly available dataset, ABD, where the results outperform other state-of-the-art (SOTA) methods.