Due to the complexity and uncertainty of customer demand behavior, it was often difficult to obtain satisfactory recommendation results by using the existing online commodity recommendation systems. Therefore, a network commodity intelligent recommendation model based on feature selection and deep belief network was proposed. Based on the basic structure and function of the existing recommendation systems, this paper expounded the interaction process between customers, e-commerce platforms, enterprises, and the recommendation system. By analyzing the internal relationship between customer demand and commodity recommendation, the relationship model between customer demand and commodity recommendation was established. After analyzing the characteristics of customers’ demand for goods, a data mining method was used to classify the characteristics of customers’ demand behavior, and a feature selection method based on deep belief network (DBN) was proposed to obtain the main information conducive to commodity recommendation. Finally, an e-commerce commodity recommendation algorithm based on feature selection and deep belief network was proposed. The experimental results showed that the network commodity recommendation model proposed in this paper can not only provide customers with satisfactory recommendation results but also has better performance than other traditional recommendation models. The recommendation model proposed in this paper can support different e-commerce website recommendation systems.

A face recognition model based on a multiscale feature fusion network is constructed, aiming to make full use of the characteristics of face and to improve the accuracy of face recognition. In addition, three different scale networks are designed to extract global features of faces. Multiscale cross-layer bilinear features of multiple networks are integrated via introducing a hierarchical bilinear pooling layer. By capturing some of the feature relationships between different levels, the model's ability to extract and distinguish subtle facial features is enhanced. Simultaneously, this study uses layer-by-layer deconvolution to fuse multilayer feature information, to solve the problem of losing some key features when extracting features from multilayer convolutional layers and pooled layers. The experimental results show that compared with the recognition accuracy of traditional algorithms, the recognition accuracy of the algorithm on Yale, AR, and ORL face databases is significantly improved.

The rapid development of communication and computer has brought many application scenarios to the fingerprint identification technology of communication equipment. The technology is of great significance in electronic countermeasures, wireless network security, and other fields and has been widely studied in recent years. The fingerprint identification technology of communication equipment is mainly based on the fingerprint characteristics represented on the transmitted signals of the equipment, which are different from other devices, and the connection between the characteristics and the hardware equipment is established, so as to realize the purpose of identifying the communication equipment. In this paper, the author studies the key technologies related to fingerprint recognition of communication equipment, including signal acquisition, signal feature extraction, and classifier design, and transient signal recognition equipment. In this paper, the integrated learning and deep learning based on fingerprint recognition are taken as the main research contents of communication equipment, and the fingerprint recognition scheme of communication equipment is given; the proposed scheme is verified by the measured data. Aiming at the transient signal of communication equipment, an algorithm using the short-term periodicity of signal is presented. The feature extraction of steady-state signal is realized. The autoencoder feature and four kinds of integral bispectrum feature are analyzed and visualized. Research on communication equipment individual recognition technology is based on ensemble learning. An individual recognition scheme for communication devices based on Extreme Gradient Boosting (XGBoost) classification model is studied. The Gradient Boosting Decision Tree (GBDT) model with different parameters was used as the primary learner of stacking classifier. The steady-state signal recognition of mobile phones based on deep learning is studied. The results show that the stacking recognition rate improved by about 2% compared with GBDT using multiple GBDT models with different parameters as the primary learner.