In recent years, virtual staining technology based on deep learning has gradually emerged as an important research direction to replace conventional histological staining by digitally generating high-quality staining images while offering advantages of short time-consuming, low cost, and high fidelity. This paper reviews and discusses the overall process of virtual staining technology based on four key stages: data acquisition, preprocessing, network design and training, and staining-quality evaluation. Despite the substantial potential of this technology in clinical applications, breakthroughs are required to improve its staining quality and clinical evaluation. In the future, the introduction of technologies such as software and hardware collaborative optimization, improvements of deep-learning preprocessing methods, and reinforcement learning is expected to propel the clinical application of virtual staining technology and promote its wide application in pathology diagnosis and medical research.