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 significances 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 field 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.