To address the problems of high pilot overhead and low accuracy in channel estimation of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface （STAR-RIS）-assisted hybrid-field communication systems， a Multi-scale Feature-aware Fusion Super Resolution Network （MFSRN） was proposed. In the network， channel estimation problem was modeled as a channel matrix reconstruction task. First， common features were extracted from an upsampled channel matrix； then， an attention gate was utilized to separate user-specific channel features； finally， reconstruction of multi-user channel matrices was completed in parallel. To enhance network performance， an Enhanced Convolution Block （ECB） and a Dual-Stream Feature-Aware （DSFA） block were designed to extract both local and global features of hybrid-field channel effectively， and a Feature Pyramid Attention （FPA） block was introduced to strengthen model’s capability for multi-scale feature aggregation. Experimental results demonstrate that compared with classic DRSN and U-MLP schemes， the proposed scheme achieves a lower pilot overhead and has the Normalized Mean Square Error （NMSE） reduced by an average of 3.62 dB and 1.28 dB respectively at different Signal-to-Noise Ratio （SNR）， showing better estimation performance.