Aiming at the problem that noise increases the error probability of the transmission signals of nonlinear digital communication system， a multivariate communication system based on discrete Bidirectional Associative Memory （BAM） neural network was proposed. Firstly， the appropriate number of neurons and memory vectors were selected according to the signals to be transmitted， the weight matrix was calculated， and BAM neural network was generated. Secondly， the multivariate signals were mapped to the initial input vectors with modulation amplitude and continuously input into the system. The input was iterated through the neural network and Gaussian noise was added to each neuron. After that， the output was sampled according to the code element interval， and then transmitted in the lossless channel， and the decision was decoded by the receiver according to the decision rule. Finally， in the field of image processing， the proposed system was used to transmit the compressed image data and decode the recovered image. Simulation results show that for weakly modulated signals with large code element interval， with the increase of noise intensity， the error probability firstly decreases and then increases， and the stochastic resonance phenomenon is relatively obvious. At the same time， the error probability is positively correlated with the radix number of the signal， and negatively correlated with the signal amplitude， code element interval and the number of neurons. Under certain conditions， the error probability can reach 0. These results show that BAM neural network can improve the reliability of digital communication system through noise. In addition， the similarity of the image restored by decoding shows the improvement of moderate noise on image restoration effect， extending the application of BAM neural network and stochastic resonance in image compression coding.