Abstract: It is of great significance to correctly evaluate the importance of news in national newspapers and magazines for better understanding the changes of national policies. In this paper, we take People’s Daily as an example, extract news published in 1946−2008, and construct news network by using their content-based similarities. In the view of complex network, news has higher similarities with others, making it be closely connected and larger nodal centrality in news network. In respect to this, we propose an H-PageRank ranking algorithm by introducing the H-index to improve the PageRank ranking algorithm. In the experiment, all news in People’s Daily is divided into four stages according to their styles and editions in different governing times, which is respectively used to construct news networks based on representation learning. The experimental results show that 1) the topologies of four news networks all have a general properties of complex network, including the high clustering coefficients, positive assortativity coefficients and approximately power-law degree distributions; 2) each news network presnets a mostly similar AUC calculated by the global rank score of the front-page news according to diverse nodal centralities, however the precision, recall and F1-score calculated by the Top-N evaluating model according to the H-PageRank centrality are optimal, which validate the efficiency of local ranking news according to the H-PageRank centrality; 3) the precision of each news network is significantly superior to the theoretical baselines even when the ranking list is restricted into different length, which suggests the roubustness of evaluating model.