According to the advantages of deep learning model in the extraction of abstract concept

a new Deep Boltzmann Machine is designed based on the outperform model-Replicate Softmax Model. Based on Replicate Softmax Model and structure of the new Deep Boltzmann Machine

energy function of this model is proposed and the detail learning algorithm is introduced. The learning can be made more efficient by using a layer-by-layer "pre-training" phase that allows variational inference to be initialized with a single bottom up pass. The values of the latent variables in the deepest layer are easy to infer and give a much better representation of each document than low learning. The 20-newsgroups document sets experiment results illustrated that the novel algorithm learn good generative models

get the better competence of a shallow model- Replicate Softmax Model in handling with an extract abstract concept and has good feasibility in large scale document analysis.