Phase-sensitive optical time domain reflectometry is widely used in perimeter security and other fields because of its advantages of wide monitoring range and high sensitivity. In recent years， researchers have improved optical systems to increase sensing distance and spatial resolution， thus greatly increasing the amount of data that needs to be processed. In addition， strong environmental noise and diverse types of vibrations bring challenges to the practical application of distributed vibration sensing systems. This study summarizes the signal processing methods used to improve the signal-to-noise ratio and vibration recognition rate of the system， including noise reduction algorithms， feature extraction algorithms， machine learning， and deep learning algorithms； compares the advantages and disadvantages of different algorithms； and finally outlines the possible direction of signal processing methods in this field in the future.