News recommendation model and application in the ERA of integrated media based on knowledge graph

In the era of integrated media, the integration of news information has demonstrated an explosive proliferation. In this era, users cannot quickly and accurately find news of interest, and the importance of personalized news recommendations has become increasingly prominent. The model based on knowledge graphs can capture rich semantic and structural information and analyze users’ personalized preferences more accurately. It is becoming the latest technology in recommendation systems. Based on the knowledge of knowledge graphs, this paper first studies the construction of knowledge graphs, including natural language processing, distributed computing, knowledge fusion, knowledge computing, and entity alignment. Then it researches the construction of mathematical models, including extracting word vectors, calculating user interest preference features, generating news feature vectors, convolutional neural network recommendation models, and integrating attention propagation layers. Finally, this paper designs and completes the simulation experiment. Using the Adressa data set, through the two evaluation indexes of F1 and AUC, the ANCNN model constructed in this paper is compared with the four benchmark models of DeepFM, DKN, NAML, and NPA, and the advantages of the ANCNN model constructed in this paper are verified. The research results have improved the efficiency of news selection and the reading experience.