Restricted Boltzmann machine (RBM) is a probabilistic undirected graph, and most traditional RBM models assume that their hidden layer units are binary. The advantage of binary units is their calculation process and sampling process are relatively simple. However, binarized hidden units may bring information loss to the process of feature extraction and data reconstruction. Therefore, a key research point of RBM theory is to construct real-valued visible layer units and hidden layer units, meanwhile, maintain the effectiveness of model training. In this study, the binary units are extended to real-valued units to model data and extract features. To achieve this, specifically, an auxiliary unit is added between the visible layer and the hidden layer, and then the graph regularization term is introduced into the energy function. Based on the binary auxiliary unit and graph regularization term, the data on the manifold has a higher probability to be mapped as a parameterized truncated Gaussian distribution, simultaneously, the data far from the manifold has a higher probability to be mapped as Gaussian noises. The hidden units can be sampled as real-valued units from the parameterized Gaussian distribution and Gaussian noises. In this study, the resulting RBM based model is called restricted Boltzmann machine with auxiliary units (ARBM). Moreover, the effectiveness of the proposed model is analyzed theoretically. The effectiveness of the model in image reconstruction task and image generation task is verified by experiments.