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Accurate mapping between single‐cell RNA sequencing (scRNA‐seq) and low‐resolution spatial transcriptomics (ST) data compensates for both limited resolution of ST data and missing spatial information of scRNA‐seq. Celloc, a method developed for this purpose, incorporates a graph attention autoencoder and comprehensive loss functions to facilitate flexible single cell‐to‐spot mapping. This enables either the dissection of cell composition within each spot or the assignment of spatial locations for every cell in scRNA‐seq data. Celloc's performance is benchmarked on simulated ST data, demonstrating superior accuracy and robustness compared to state‐of‐the‐art methods. Evaluations on real datasets suggest that Celloc can reconstruct cellular spatial structures with various cell types across different tissues and histological regions.