There are relatively few studies on the multicoil reconstruction task of existing Magnetic resonance imaging (MRI) methods, as there are problems with insufficient reconstruction details, high memory occupation during training, etc. Therefore, a new Dual domain Parallel Fusion Reconstruction Network (DPFNet) is proposed in this paper. The whole network consists of coil sensitivity graph estimation module, dual domain feature extraction module, dual domain dynamic error correction module, and dual domain dynamic fusion module. A U-Net has been used as the backbone network. The network reconstructs undersampled MRI images and K-space data simultaneously in two branches of the image domain and K-space domain, and the fusion module realizes the reconstruction information interaction between the two branches. In addition, a new dual domain consistency loss is also proposed, which reduces the error between the same MRI slice image and K-space data with dual domain output, and achieves high quality reconstruction. In this paper, a series of comparative experiments and ablation experiments are conducted in the open Calgary-Campinas-359 brain MRI data set. The results of the experiments show that the proposed DPFNet achieves the most advanced level at present and is superior to other traditional algorithms and reconstruction methods based on deep learning. In particular, the reconstruction results from Cartesian sampling are very good.