To improve the performance of personalized recommendation system， a personalized recommendation method based on depth-restricted Boltzmann machine is proposed. Firstly， by extracting the characteristics of users and resources of the recommendation system， a multi-layer restricted Boltzmann machine （RBM） network is constructed， thus forming a personalized recommendation model of depth-restricted Boltzmann machine. Secondly， the maximum likelihood of the training samples to be recommended is calculated by the marginal probability distribution of visible and hidden layers. Then， combined with contrastive divergence （CD） reconstruction， the main parameter updating mode of RBM is obtained， and the stable RBM structure is obtained by updating the visible and hidden layers in both directions. Finally， the personalized recommendation is realized by calculating the user resource score. Experimental results show that， within the reasonable range of sparsity of training samples， compared with the commonly used personalized recommendation algorithms， the proposed method can obtain better root mean squared error （RMSE） performance by reasonably controlling the depth of RBM and setting the appropriate number of hidden layer nodes.