Fourier ptychography microscopy (FPM) is limited by hardware and algorithm, and its overall performance needs to be improved. To address the issues of slow imaging speed and low imaging quality of traditional FPM technology, the FPM image reconstruction approach integrated with depth learning has been widely explored. Herein, based on this, a super-resolution countermeasure generation network-based FPM model is proposed. Furthermore, global feature fusion is obtained by adding dense block connections using the original network, and a weighted loss function is used to enhance the quality of image reconstruction. The reconstruction results of the resolution plate image demonstrate that the proposed depth learning method has a better reconstruction effect and faster reconstruction speed than the conventional method.