Fourier Ptychography (FP) is a technology of achieving high-resolution, large field-of-view imaging of optical system. However, the high-resolution reconstruction based on traditional FP methods requires a high aperture overlap ratio, resulting in a large number of captured images and low sampling efficiency. In addition, the FP reconstruction algorithm has high complexity and long reconstruction time. Aiming at solving these problems of the FP, this paper proposes a deep learning algorithm based on multi-scale feature fusion network. Through the improved feature pyramid module, the feature information can be extracted from multiple low-resolution images captured by the FP imaging system, and the information is fused to achieve super-resolution reconstruction. Experimental results show that compared with traditional methods, the deep learning algorithm proposed in this paper improves the quality of image reconstruction, reduces the reconstruction time by 90%, and is more robust to Gaussian noise. In addition, the proposed method can reduce the overlap ratio between sub-apertures from 50% to 25% in frequency domain, and reduce the number of captured images by 50%, greatly improving the sampling efficiency.