Purpose To introduce a dual‐domain reconstruction network with V‐Net and K‐Net for accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of‐the‐art reconstruction methods apply U‐Net or cascaded U‐Nets in the image domain and/or k‐space domain. Nevertheless, these methods have the following problems: (1) directly applying U‐Net in the k‐space domain is not optimal for extracting features; (2) classical image‐domain–oriented U‐Net is heavyweighted and hence inefficient when cascaded many times to yield good reconstruction accuracy; (3) classical image‐domain–oriented U‐Net does not make full use of information of the encoder network for extracting features in the decoder network; and (4) existing methods are ineffective in simultaneously extracting and fusing features in the image domain and its dual k‐space domain. To tackle these problems, we present 3 different methods: (1) V‐Net, an image‐domain encoder–decoder subnetwork that is more lightweight for cascading and effective in fully utilizing features in the encoder for decoding; (2) K‐Net, a k‐space domain subnetwork that is more suitable for extracting hierarchical features in the k‐space domain, and (3) KV‐Net, a dual‐domain reconstruction network in which V‐Nets and K‐Nets are effectively combined and cascaded. Results Extensive experimental results on the fastMRI dataset demonstrate that the proposed KV‐Net can reconstruct high‐quality images and outperform state‐of‐the‐art approaches with fewer parameters. Conclusions To reconstruct images effectively and efficiently from incomplete k‐space data, we have presented a dual‐domain KV‐Net to combine K‐Nets and V‐Nets. The KV‐Net achieves better results with 9% and 5% parameters than comparable methods (XPD‐Net and i‐RIM).