In recent years, fluorescence microscopy has been commonly applied in various fields of scientific research, such as biophysics, neuroscience, cell biology, and molecular biology, owing to its specificity, high contrast, and high signal-to-noise ratio. However, traditional fluorescence microscopes have limitations regarding spatial resolution, imaging speed, field of view, phototoxicity, and photobleaching; these limitations compromise their applications in subcellular observation, in vivo imaging, and molecular structure profiling. To moderate such limitations, researchers have adopted data-driven deep learning methods, which can enrich the existing fluorescence microscopy technologies and boost the performance boundary of traditional fluorescence microscopy. This article focuses on the technologies and applications of deep learning based fluorescence microscopy. First, we briefly summarize the basic principle and development path of deep learning technologies; then, we introduce the latest domestic and global progress of deep learning based fluorescence microscopy. Compared with the traditional microscopic imaging system, we show the superiority of deep learning in solving fluorescence microscopy problems. Finally, the future potential of developing deep learning based microscopy is highlighted.