# An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer
## 1 Introduction  
As a type of deep neural network, deep belief network (DBN) has a powerful predictive classification function. DBN is widely used in the field of prediction, such as recommendation systems. Nowadays, the Internet is developing rapidly, and it has become a big problem to obtain the desired resources from massive resources in a short period of time, and the recommendation system came into being. Currently, recommendation systems have been widely used on e-commerce platforms. With the continuous development of the Internet, more and more people are accustomed to conducting commodity transactions through the Internet. Recommendation systems can not only satisfy user interests, but also bring profits to enterprises. More and more e-commerce websites use recommendation systems, such as Amazon product recommendation, Douban music recommendation, news recommendation, etc. Therefore, using deep neural networks to improve the prediction accuracy of recommendation systems is the future development trend of the Internet. In the field of deep learning, the internal structure of sample data is characterized by establishing a multi-level representation model, that is, by extracting high-level features layer by layer, so as to obtain a more abstract object representation [1]. The research of deep learning has a great impact in the field of artificial intelligence [2]. Deep models such as DBNs (deep belief networks) [3], convolutional neural networks (CNNs) [4] and recurrent neural networks (RNNs) [5] have been maturely applied to machine learning, pattern recognition, feature extraction and data mining. Inspired by deep learning and hierarchical representation, Professor [PERSON] proposed DBN as a generation model composed of multi-layer nonlinear neural units [6; 7]. Through unsupervised learning and supervised learning,it can be applied to feature learning, prediction classification and image processing of sample data [8]. However, descriptions of how to determine the structure and size of the model are rarely seen in the literature. The network structure of DBN is generally derived from experience. If the network structure (including the number of neurons in each layer and the number of hidden layers) is set, it will not change, and doing so will bear a higher computational cost [9]. [PERSON] et al. [10] determined the DBN structure through repeated manual experiments, but its model operation efficiency was low and the model performance was poor. However, the complexity of the network structure determines the ability to solve complex problems. Therefore, the more complex the network structure of DBN, the stronger the ability to solve complex problems. In the process of training DBN, the larger the number of neurons and the deeper the hidden layer, the more difficult the training and the longer the corresponding training time. At the same time, training errors are also accumulating, resulting in low accuracy of the model. In practical applications, DBN needs to build different network structures for different task scenarios. However, due to the lack of corresponding theoretical support and efficient training methods, researchers need to set network parameters based on experience, resulting in low model operation efficiency and poor model performance during the modeling process [11]. In order to prevent DBN from low training accuracy or data overfitting, it is very necessary for DBN to independently construct the best network structure. Therefore, how to independently construct the best network structure of DBN and obtain good performance has become a problem worth studying. With the in-depth research on the DBN model, many researchers have proposed a variety of methods to improve the DBN model. [PERSON] et al. [12] proposed an adaptive step size technology to control the model learning rate, thereby improving the convergence of the contrastive divergence (CD) algorithm. Specifically, the learning rate of the model is controlled according to the update direction of the model parameters after iteration.