Alternative polyadenylation (APA) can lead to various mRNA isforms differing in their 3' UTR, which contributes to the dynamics of gene regulation, including stability, localization and translation of mRNA. However, clustering of genes using poly(A) site data has not been extensively studied. Here we constructed a two-layer model based on canonical correlation analysis (CCA) to explore the clustering of genes with the consideration of the distribution and abundance of APA sites. We adopted the widely used hierarchical clustering with Average-Linkage (Hc-A), Complete-Linkage (Hc-C), and Single-Linkage (Hc-S) to comprehensively compare the proposed method with four distance measures including Pearson correlation coefficient (PCC), Euclidean distance (EUC), Manhattan distance (MAN), and Maximum distance (MAX) based on three performance indexes. Results showed that the proposed method performed better than other four distance measures and could generate clusters with more biological meaning and underlying molecular mechanisms.