Aiming at the problem that existing super-resolution reconstruction algorithms were difficult to fully utilize multi-scale information and deep features of images， an image super-resolution reconstruction method based on multi-scale deep feature distillation （MSDFDN） was proposed. First， ConvNeXt convolution was used to replace traditional convolution layers， increasing network depth with lower computational cost to improve performance. Second， a multi-scale deep feature distillation module was designed. By constructing ConvNeXt convolution layers of different scales and combining them with a residual feature distillation mechanism， multi-scale deep features in residual blocks were extracted while bypassing rich low-frequency information. Finally， an attention mechanism was introduced at the end of the module to adaptively weight extracted features， enabling the network to focus more on high-frequency information. Compared with other advanced lightweight super-resolution reconstruction algorithms on benchmark datasets and the self-built PDC bit composite dataset， the peak signal-to-noise ratio and structural similarity quantitative metrics of images obtained by this method showed improvement. Especially on the Urban100 dataset with more detailed information， the peak signal-to-noise ratio of the four-fold reconstructed image reaches 26.49 dB， and the structural similarity reaches 0.797 6. Experimental results show that the proposed method has better objective and subjective measurement results.