A segmentation network of heart magnetic resonance image that combines prior knowledge in the frequency domain and feature fusion enhancement is proposed to address the issues of unclear boundaries caused due to the small grayscale differences among the heart substructures in heart magnetic resonance images and the varying shapes and sizes of the right ventricular region, affecting segmentation accuracy. The proposed model is a D-shaped structured network comprising a frequency domain prior guidance and feature fusion enhancer subnetworks. First, the original image is transformed from the spatial domain to the frequency domain using Fourier transform, extracting high-frequency edge features and combining the low-level features of the frequency domain prior-guided subnetwork with the corresponding stages of the feature fusion enhancement subnetwork for improving the edge recognition ability. Second, a feature fusion module with local and global attention mechanisms is introduced at the jump connection of the feature fusion enhancer network to extract contextual information and obtain rich texture details. Finally, the Transformer module is introduced at the bottom of the network to further extract long-distance semantic information, enhance the expression ability of the model, and improve segmentation accuracy. Experimental results on the ACDC dataset reveal that compared to existing methods, the proposed method achieves the best results in objective indicators and visual effects. Good cardiac segmentation results can provide reference for subsequent image analysis and clinical diagnosis.