Google 的PageRank算法在排序上有着得天独厚的的优势，可以运用在各种网络分析节点重要性中。本文说明了PageRank算法的收敛性，并讨论了在引文分析中的参数具体意义，利用PageRank算法在引文网络中衡量学者学术影响力，分析PR排名在不同排名分段的排名效果，考量普赖斯奖得主排名来分析排名效果。分析不同阻尼系数下的PR排名情况的稳定性，阻尼系数α较小时PR排名越稳定，阻尼系数α较大时PR排名越波动，比较排名效果阻尼系数α越大时效果越好，综合考量阻尼系数α为0.55时最为合适。并与传统引用次数相比较，阐述分析是否存在自引不当，PageRank算法在被引次数表现不佳的分析低被引学者影响力领域表现良好。最后论述可以调整PageRank矩阵的转移矩阵中的元素来得到关心的结果，如引用矩阵中增加时间衰减因子提高新学者的排名。

Google's PageRank algorithm has unique advantages in ranking and can be used in the importance of various network analysis nodes.This paper illustrates the convergence of PageRank algorithm, and discusses the specific meaning of parameters in citation analysis.Useing PageRank algorithm to measure academic influence of scholars in citation network, analyzes the ranking effect of PR ranking in different ranking segments, considering Derek de Solla Price Memorial Medal winners’ ranking to analyze ranking effect.Analyze the stability of the PR ranking under different damping coefficients. The damping coefficient is smaller, the PR ranking is more stable.The damping coefficient is large, the PR ranking is more fluctuating.Compared with the ranking effect, the larger the damping coefficient, the better the effect.It is most suitable when the damping coefficient is 0.55.And compared with the traditional citation counts, to illustrate whether there is inappropriate self-citation in the analysis, the PageRank algorithm performs well in the analysis of low-cited scholars' influence fields with poor citation counts.Finally, it is discussed that the elements in the transition matrix of the PageRank matrix can be adjusted to obtain the results of interest, such as increasing the time decay factor in the citation matrix to improve the ranking of new scholars.