Most hidden neurons in Deep Belief Network (DBN) are noise variables, and have  group structure correlation. Group Sparse Deep Belief Network (GSDBN) constrains the implicit neuron variables via group Lasso so as to achieve variable group selection. However, the group sparse model not  only ignores the case that some features belong to multiple groups simultaneously, but also has the problem that hidden neurons are not sparse. In this paper, we propose Overlap Sparse Group Deep Belief Network (OSGDBN), which introduces the overlap group structure and makes the hidden neurons sparse, based on Group Sparse Deep Belief Network. In addition, we also explain the reason that the OSGDBN is sparser than GSDBN. The recognition results on MNIST, USPS, ETH80 and face datasets show that OSGDBN has a higher recognition rate.