To resolve the problems of feature homogeneity in unsupervised training of Restricted Boltzmann Machine (RBM) and non-adaptiveness of Sparse Restricted Boltzmann Machine (SRBM), a new sparse mechanism method of RBM based on competitive learning was designed. Firstly, a distance measurement was designed based on the cosine value between the neuron weight vector and the input vector to evaluate the similarity. Secondly, the optimal matching implicit unit based on distance measurement was selected for different samples during training. Thirdly, the sparse penalty for other hidden units was calculated according to the activation state of the optimal matching hidden unit. Finally, the parameters were updated and the competitive sparseness was applied to the construction of Deep Boltzmann Machine (DBM) based on the deep model training process. The handwritten number recognition results show that, compared with the mechanism using the sum of squared errors as the regularization factor, the classification accuracy of DBM based on new sparse mechanism is improved by 0.74%, and the average sparsity measurement is increased by 5.6%, without the need to set sparse parameters. Therefore, the proposed sparse mechanism can improve the training efficiency of unsupervised training model, such as RBM, and can be applied into the construction of deep models.