Abstract：In the research of channel decoding combined with deep learning technology, the problem of dimension limitation has al-
ways been the focus of researchers. Since the deep neural network is storage intensive, the channel decoder of deep neural network
usually needs much more computation consumption and memory than the conventional belief-propagation (BP) decoding. In order
to alleviate this problem, an improved neural network decoder for LDPC code is proposed. According to the weight parameter value
distribution in the deep neural network channel decoder, weight parameters are selectively added to a new neural network decoder.
By limiting the number of training parameters, the scale of the deep neural network channel decoder is reduced. And our algorithm
gets a large decoding gain than BP decoding.
