  With the continuous maturation and expansion of neural network technology,
deep neural networks have been widely utilized as the fundamental building
blocks of deep learning in a variety of applications, including speech
recognition, machine translation, image processing, and the creation of
recommendation systems. Therefore, many real-world complex problems can be
solved by the deep learning techniques. As is known, traditional news
recommendation systems mostly employ techniques based on collaborative
filtering and deep learning, but the performance of these algorithms is
constrained by the sparsity of the data and the scalability of the approaches.
In this paper, we propose a recommendation model using granular neural network
model to recommend news to appropriate channels by analyzing the properties of
news. Specifically, a specified neural network serves as the foundation for the
granular neural network that the model is considered to be build. Different
information granularities are attributed to various types of news material, and
different information granularities are released between networks in various
ways. When processing data, granular output is created, which is compared to
the interval values pre-set on various platforms and used to quantify the
analysis's effectiveness. The analysis results could help the media to match
the proper news in depth, maximize the public attention of the news and the
utilization of media resources.
