The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized gene expression studies at the single-cell level. However, the presence of technical noise and data sparsity in scRNA-seq often undermines the accuracy of subsequent analyses. Existing methods for denoising and imputing scRNA-seq data often rely on stringent assumptions about data distribution, limiting the effectiveness of data recovery. In this study, we propose the scDMAE model for denoising and recovery of scRNA-seq data. First, the model fuses gene expression features and topological features to discern the primary expression patterns of genes in cells. Then, an autoencoder with a masking strategy is used to model dropout events and separate potential noise in the data. Finally, the model incorporates the original raw data to recover the true biological expression value. By conducting experiments on various types of scRNA-Seq datasets, scDMAE demonstrates superior performance compared to other comparative methods based on six distinct evaluation metrics in downstream analysis. The scDMAE method can accurately cluster similar cell populations, identify differential genes and infer cell trajectories.