A Siamese Neural Network (SNN) is a specialized deep learning framework characterized by weight sharing and similarity learning. Compared with traditional neural networks, it overcomes the limitations of heavy dependence on large-scale labeled datasets and poor performance in similarity comparison tasks. Owing to their strong capability in feature representation and metric learning, SNNs have been widely applied in pattern recognition, object tracking, medical diagnosis, and few-shot learning. This paper introduces the Siamese Neural Networks from four aspects: fundamental concepts and network structures, improvement strategies and key technologies, major application fields, and future development trends. Furthermore, the advantages and current limitations of SNNs are summarized, providing a reference for subsequent theoretical research and engineering applications.