Electrical impedance tomography ( EIT) provides an effective method for monitoring the spatial features of human lungs because of its non-invasiveness and visualization natures. However, the inverse problem of EIT has serious non-linearity, ill-posedness and indeterminate feature, which makes the reconstructed images contain serious artifacts. Aiming at the above problems, a deep network imaging algorithm of V-ResNet composed of pre-mapping module, feature extraction module, deep reconstruction module and residual denoising module is proposed in this paper, which achieves the reconstruction of the spatial position and conductivity parameter distributions of the field. This algorithm can effectively increase the feedforward information by multiple transmissions and solve the phenomenon of gradient disappearance in deep networks. Meanwhile, the residual denoising module is utilized to effectively smooth the image boundary. The relative error (RE) and structural similarity (SSIM) are used to evaluate the imaging quality, and the experiments show that the average RE is 0. 14 and the average SSIM is 0. 96. The results of the simulations and experiments illustrate that compared with traditional imaging algorithms, the imaging algorithm based on V-ResNet achieves clearer boundaries and higher resolution in imaging result.