Inflammation plays a central role in knee osteoarthritis (OA) pathogenesis (C. R. Scanzello, 2017). The synovial membrane inflammation is associated with disease progression and represents a primary source of agony in knee OA (L. A. Stoppiello et al., 2014). Many inflammatory mediators may have biomarker utility. To identify synovium related to knee OA pain biomarkers, we used canonical correlation analysis to analyze the miRNA-mRNA dual expression profiling data and extracted the miRNAs and mRNAs. After identifying miRNAs and mRNAs, we built an interaction network by integrating miRWalk2.0. Then, we extended the network by increasing miRNA-mRNA pairs and identified five miRNAs and four genes (TGFBR2, DST, TBXAS1, and FHLI) through the Spearman rank correlation test. For miRNAs involved in the network, we further performed the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, whereafter only those mRNAs overlapped with the Online Mendelian Inheritance in Man (OMIM) genetic database were analyzed. Receiver operating characteristic (ROC) curve and support vector machine (SVM) classification were taken into the analysis. The results demonstrated that all the recognized miRNAs and their gene targets in the network might be potential biomarkers for synovial-associated pain in knee OA. This study predicts the underlying risk biomarkers of synovium pain in knee OA.