The calcific aortic valve disease (CAVD) develops as an aortic valve sclerosis and progresses to an advanced form of stenosis. In many biological fields, bioinformatics becomes a fundamental component. The key mechanisms involved in CAVD are discovered with the use of bioinformatics to investigate gene function and pathways. We downloaded the original data (GSE51472) from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). After standardization, 2978 differentially expressed genes (DEGs) were identified from the data sets GSE51472 containing samples from normal, calcified, and sclerotic aortic valves. Analysis of DEGs based on the series test of clusters (STCs) revealed the two most significant patterns. Based on the result of the STC, the functional enrichment analysis of gene ontology (GO) was conducted to investigate the molecular function (MF), biological process (BP), and cell compound (CC) of the DEGs. With a value of 0.01, DEGs associated with "chronic inflammation," "T-cell receptor complexes," and "antigen binding" had the highest significance within BP, CC, and MF. DEG enrichment in signaling pathways was analyzed using KEGG pathway enrichment. Using a  < 0.05 level of significance, the most enriched biological pathways related to CAVD were "Chemokine signaling pathway," "Cytokine-cytokine receptor interaction," "Tuberculosis," "PI3K-Akt signaling pathway," and "Transcriptional misregulation in cancer." Finally, the construction of gene co-expression networks and pathway networks illustrated the pathogensis of CAVD. , , and were identified as hub genes for the development of CAVD. Moreover, "MAPK signaling pathway," "Apoptosis," and "Pathways in cancer" were regarded as the core pathways among the samples of normal, sclerotic and calcified aortic valve samples.