Existing single-image super-resolution algorithms lose high-frequency details and cannot extract rich image features. Therefore， an image super-resolution reconstruction algorithm based on a multi-scale dense feature fusion network is proposed to efficiently utilize image features. This algorithm extracts image features of different scales by employing the multi-scale feature fusion residual module with convolution kernels of different scales. It fuses different scale features to better preserve the high-frequency details of images. A dense feature fusion structure is adopted between modules to fully integrate the feature information extracted from different modules， to avoid feature information loss and obtain better visual feeling. Several experiments show that the proposed method can significantly improve the peak signal-to-noise ratio and structural similarity on four benchmark datasets while reducing the number of parameters. In particular， on the Set5 dataset， compared with DID-D5， the peak signal-to-noise ratio of 4× super-resolution increases by 0.08 dB and the reconstructed image has better visual effects and richer feature information， thus confirming the effectiveness of the proposed algorithm.