In order to solve the current problems of ignoring individual user differences, rising marketing costs, and customer churn in the crude marketing process, we combine deep machine learning technology and the long-term operational experience of Media Network Co. deep learning. By extracting features from the network data in image format, DNSI can obtain three kinds of neighbor structure influence. The optimal graph theory approach is used to construct user intelligence graphs, and then deep machine learning techniques are used to achieve accurate marketing in combination with different scenarios. Experimental results on several real-world network data sets for tasks such as node attribute prediction, category centrality measure, and user behavior prediction show the superiority of the model in most cases.