Objective Facing the challenges posed by population aging, the incidence of age-related neurodegenerative diseases continues to rise, yet their pathogenesis remains elusive with limited therapeutic options. This study focuses on aging as the fundamental basis of disease onset, employing bioinformatic approaches to explore cell-type-specific changes in gene expression during brain aging and their impacts, thereby providing further insights into the biological mechanisms of brain aging. Methods We utilized the Seurat package in R software to integrate, quality control, normalize, and statistically analyze single-cell sequencing datasets (GSE169606) from young and old mouse brains. Through cell type annotation and differential gene expression analysis, we identified differentially expressed genes (DEGs) across various cell types. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional annotation and enrichment analysis, and the interactions between differentially expressed genes were analysed by Protein-Protein Interaction (PPI). Finally, the four algorithms of MCC, MNC, DMNC and Dgree from the cytoHubba plugin was employed to identify the hub genes in each cell. Results A total of 13 cell types were identified through cell annotation. After comparing the aged group with the young group, we focused on in-depth analyses of the differential genes screened from four major cell types—neurons, microglia, astrocytes, and endothelia. GO analysis revealed that the differential genes in neurons, astrocytes, and endothelial cells were significantly enriched in biological pathways related to circadian rhythm. KEGG analysis found that the differential genes in microglia and endothelial cells were enriched in circadian rhythm-related signaling pathways. PPI analysis demonstrated that the biological networks of differential genes in neurons, microglia, and endothelial cells were significantly enriched in circadian rhythm functional clustering modules. Furthermore, by taking the intersection of four algorithms, we identified core genes within these cell types, and in the process, we also discovered specific variations of circadian rhythm genes in microglia, astrocytes, and endothelial. Conclusion This study employed single-cell transcriptomics technology to reveal the differential expression of genes in neurons, microglia, astrocytes, and endothelial during aging. Hub genes in microglia, astrocytes and endothelial were identified, especially the specific changes in circadian rhythm genes across these three cell types. These findings provide a foundation for further exploring the molecular mechanisms of brain aging and developing related intervention strategies.