Because of hyperspectral imaging equipment are expensive， a deep learning network to reconstruct high-quality hyperspectral images from easily obtained RGB images was proposed. The proposed network was based on the Unet framework， and its backbone network was primarily constructed using the Res2Net module， which could extract fine local and global image features. The channel attention mechanism was introduced to adaptively adjust the channel characteristic response， and the information of different scales and depths was fully integrated through a skip connection between the coding and decoding paths. Finally， it was trained and tested on the dataset provided by the new trends in the image restoration and enhancement （NTIRE） 2020 international challenge. Experiments show that compared with the adaptive weighted attention network （AWAN） and hierarchical regression network （HRNet）， the proposed method obtains the best results in the four objective evaluation methods， such as the mean of relative absolute error （MRAE）， root mean square error （RMSE）， peak signal-to-noise ratio （PSNR）， and mean of spectral angle mapper （MSAM）. Compared with AWAN and HRNet， the proposed method improves the mean of the PSNR by 0.08 dB and 1.73 dB， respectively， on the clean track， and 0.72 dB and 0.97 dB， respectively， on the real-world track. The proposed method reconstructs images with better subjective quality in the low-frequency flat area and the high-frequency texture area than the hyperspectral reference images.