The rapid development of single-cell RNA sequencing technology has opened new horizons for biomedical research. As the initial step in single-cell data analysis, clustering forms the foundation for downstream analyses. However, due to the inherent high dimensionality, noise, and sparsity of single-cell data, most current clustering methods exhibit poor stability. To address these limitations, we propose a novel clustering method based on multi-view generation (scCMVG) coupled with noise perturbation. Specifically, scCMVG introduces disturbances at both gene level and cellular level to disrupt the original associations between genes and cells. It then employs autoencoder and graph convolutional network to reconstruct the disturbed data and generate new feature representations from it. Through this strategy, the model's noise resistance can be enhanced, increasing the ability to capture diverse cellular states and gene expression patterns. Experimental results demonstrate scCMVG superior clustering performance across multiple single-cell RNA sequencing datasets, outperforming existing methods. Consequently, scCMVG emerges as a valuable tool for clustering applications.