Fluorescence microscopy has the advantage of minimal invasion to bio-samples and visualization of specific structures, and therefore, it has been acting as one of mainstream imaging tools in biomedical research. With the rapid development of artificial intelligence technology, deep learning that has outstanding performance in solving sorts of inverse problems has been widely used in many fields. In recent years, the applications of deep learning in fluorescence microscopy have sprung up, bringing breakthroughs and new insights in the development of fluorescence microscopy. Based on the above, this paper first introduces the basic networks of deep learning, and reviews the applications of deep learning in fluorescence microscopy for improvement of spatial resolution, image acquisition and reconstruction speed, imaging throughput, and imaging quality. Finally, we summarize the research on deep learning in fluorescence microscopy, discuss the remaining challenges, and prospect the future work.