  The lensing effect of the cosmic microwave background (CMB) is a powerful
tool for our study of the distribution of matter in the universe. Currently,
the quadratic estimator (EQ) method, which is widely used to reconstruct
lensing potential, has been known to be sub-optimal for the low-noise levels
polarization data from next-generation CMB experiments. To improve the
performance of the reconstruction, other methods, such as the maximum
likelihood estimator and machine learning algorithms are developed. In this
work, we present a deep convolutional neural network model named the Residual
Dense Local Feature U-net (RDLFUnet) for reconstructing the CMB lensing
convergence field. By simulating lensed CMB data with different noise levels to
train and test network models, we find that for noise levels less than
$5\mu$K-arcmin, RDLFUnet can recover the input gravitational potential with a
higher signal-to-noise ratio than the previous deep learning and the
traditional QE methods at almost the entire observation scales.
