The algorithm used for reconstruction or resolution enhancement is one of the factors affecting the quality of super-resolution images obtained by fluorescence microscopy. Deep-learning-based algorithms have achieved state-of-the-art performance in super-resolution fluorescence microscopy and are becoming increasingly attractive. We firstly introduce commonly-used deep learning models, and then review the latest applications in terms of the network architectures, the training data and the loss functions. Additionally, we discuss the challenges and limits when using deep learning to analyze the fluorescence microscopic data, and suggest ways to improve the reliability and robustness of deep learning applications.