In recent years, the theory of deep learning is booming again. It is widely used in machine learning, visual recognition and auditory recognition. Boltzmann machine is a typical deep learning neural network. There are many training algorithms for its net- work weights, such as contrast dispersion (CD) algorithm, which is classical. However, the current algorithm cannot accurately ob- tain the expected value of network thermal equilibrium state. Only approximate gradient values can be calculated. At the same time, the algorithm has a large amount of computation and a long running time. In this paper, a method of RBM weight calculation is pro- posed. Firstly, RBM is equivalent to Hopfield network. Then the weight matrix is designed by DHNN weight design method. Final- ly, the RBM weight solving problem is transformed into the eigenvalue and eigenvector problem of DHNN weight matrix. An exam- ple is given to illustrate the calculation process and the correctness of the algorithm is verified by the data.