Fourier ptychographic microscopy (FPM) is a newly developed imaging technology, which is capable of reconstructing images with a wide field of view and high resolution. However, the reconstruction based on traditional reconstruction algorithms has high calculation cost, large amount of image acquisition, and low efficiency. Therefore, we propose a deep learning-based neural network model of FPM that performs end-to-end mapping from low-resolution to high-resolution to effectively improve imaging performance and efficiency. First, the diamond sampling method is used to speed up the process of image acquisition. Second, the combination of residual structure, dense connection, and channel attention mechanism is used to expand the network depth, mine useful features, and enhance the expression and generalization ability of the network model. Then, sub-pixel convolution is used for efficient upsampling and restoring high-resolution images. Finally, subjective and objective evaluation methods are used to evaluate the reconstruction results. The results show that, compared with the traditional reconstruction algorithm, the proposed network model has better reconstruction effect, lower computational complexity, and shorter average reconstruction time. At the same time, the number of low-resolution images is reduced by about half compared with the traditional algorithm.