随着大数据时代的来临，microRNA与基因的序列数据不断增加，如何从大量的数据中挖掘有生物学意义的信息成为新的热点问题。研究表明microRNA间以协作的方式在疾病中发挥作用，并呈现出网络的结构化趋势。因此，系统分析不同 microRNA间的相似性将在疾病生物学标记挖掘等研究领域起到关键的桥梁作用。而microRNA通过调节其靶基因发挥作用，所以本研究将充分利用现有靶基因数据，从功能角度分析 microRNA间的相似性。研究选取前期工作所得的靶基因优化列表，利用富集分析将基因集合转化为功能节点集合，并在此基础上利用集合相似性测度计算microRNA对在不同层面的功能一致性。结果表明，相同家族的microRNA倾向于调控相同或相似的靶基因；类比于非靶基因，microRNA靶基因倾向于共享较多相似的细胞组分，而在生物学通路及生物学过程中则具有相对较低的相似性。“,”The numbers of microRNA and genes sequences have increased greatly with the advent of big data era. Thus how to explore useful information with biological signiifcances from massive datasets has become a new hot topic. Former researches showed that microRNAs tended to play roles in diseases in a cooperative way and the relationships could be presented in the form of network. As a result, similarity analysis for microRNAs through a system way could play an important role in the ifeld of disease biomarkers discovery. Considering that microRNAs play regulation roles by binding to their target genes, we focused on the available target gene data to analyze the similarity of microRNA pairs on functional levels. The optimization microRNA targets list generated by our former research as input were chosen and the enrichment analysis was used to map gene sets into functional term sets. The similarities between microRNAs were then calculated using similarity metrics on functional levels. Our results show that microRNAs in the same family tend to regulate the same or similar target genes. Compared with non-target genes, microRNA target genes tend to share similar cellular component. However, they show fewer similarities on biological pathway and biological progress levels.