In　recent　years,　single-cell　RNA　sequencing　(scRNA-seq)　has　emerged　as　a　powerful　technique　for　investigating　cellular　heterogeneity　and　structure.　However,　analyzing　scRNA-seq　data　remains　challenging,　especially　in　the　context　of　COVID-19　research.　Single-cell　clustering　is　a　key　step　in　analyzing　scRNA-seq　data,　and　deep　learning　methods　have　shown　great　potential　in　this　area.　In　this　work,　we　propose　a　novel　scRNA-seq　analysis　framework　called　scAAGA.　Specifically,　we　utilize　an　asymmetric　autoencoder　with　a　gene　attention　module　to　learn　important　gene　features　adaptively　from　scRNA-seq　data,　with　the　aim　of　improving　the　clustering　effect.　We　apply　scAAGA　to　COVID19　peripheral　blood　mononuclear　cell　(PBMC)　scRNA-seq　data　and　compare　its　performance　with　state-of-the-art　methods.　Our　results　consistently　demonstrate　that　scAAGA　outperforms　existing　methods　in　terms　of　adjusted　rand　index　(ARI),　normalized　mutual　information　(NMI),　and　adjusted　mutual　information　(AMI)　scores,　achieving　improvements　ranging　from　2.8%　to　27.8%　in　NMI　scores.　Additionally,　we　discuss　a　data　augmentation　technology　to　expand　the　datasets　and　improve　the　accuracy　of　scAAGA.　Overall,　scAAGA　presents　a　robust　tool　for　scRNA-seq　data　analysis,　enhancing　the　accuracy　and　reliability　of　clustering　results　in　COVID-19　research.