With the rapid growth of medical literature, it is difficult for physicians to maintain up-to-date knowledge by reading biomedical literatures. An algorithm named MedRank can be used to recommend influential medications from literature by analyzing information network, based on the assumption that "a good treatment is likely to be found in a good medical article published in a good journal, written by good author(s)", recomending the most effective drugs for all types of disease patients. But the algorithm still has several problems:1) the diseases, as the inputs, are not independent; 2) the outputs are not specific drugs; 3) some other factors such as the publication time of the article are not considered; 4) there is no definition of "good" for the articles, journals and authors. An improved algorithm named HIC-MedRank was proposed by introducing H-index of authors, impact factor of journals and citation count of articles as criterion for defining good authors, journals and articles, and recommended antihypertensive agents for the patients suffered from Hypertension with Chronic Kidney Disease (CKD) by considering published time, support institutions, publishing type and some other factors of articles. The experimental results on Medline datasets show that the recommendation drugs of HIC-MedRank algorithm are more precise than those of MedRank, and are more recognized by attending physicians. The consistency rate is up to 80% by comparing with the JNC guidelines.