Restricted Boltzmann machine (RBM) is a stochastic neural network and probabilistic graphical model

which is one of the most effective models without supervision in deep learning. Focusing on the gradient approximation algorithm insensitivity to the momentum acceleration and recognition effectiveness in RBM

we propose the algorithm based on modified momentum using RBM. When the rule to update the hidden states adopts the probability value instead of sampling a binary value

this calculation method for the RBM gradient approximation leads to the undesirable recognition performance and limited momentum acceleration.Therefore

we modify the updating rule of the hidden bias to avoid these problems.Simultaneously

we use the rapidly ascending momentum method to improve the learning speed in the RBM pre-training phase. An improved slowly descending momentum method is also used in the fine-tuning stage to accurately find the best point

which is far from becoming trapped in poor local optima and improves the classification effect. Through the recognition experiments on MNIST dataset

Extended Yale B and CMU-PIE face dataset

the achieved results show that the proposed algorithm can enhance the computation efficiency and improve the generalization ability of networks. The algorithm not only extends the application fields of RBM

but also provides a new research idea and reference for the application method of deep learning.