光纤振动预警系统可自动采集周边振动信号，面对大量复杂的振动信号，如何准确识别目标振源是系统研究的难点。针对光纤振动安全预警系统采集到的振动信号进行属性特征分析，建立相应的特征模型，并建立振源属性特征模型，包括识别下雨振源的能量信息熵模型，以及区分机械施工和车辆经过振源的基频稳定性模型等。通过振源识别算法，提高了振源类型识别的准确性。测试结果表明，特征模型的设计和选择合理，识别准确率高。

One of the key technologies for optical fiber vibration pre-warning system (OFVWS) refers to identifying the vibration source accurately from the detected vibration signals. Because of many kinds of vibration sources and complex geological structures, the implement of identifying vibration sources presents some interesting challenges which need to be overcome in order to achieve acceptable performance. This paper mainly conducts on the time domain and frequency domain analysis of the vibration signals detected by the OFVWS and establishes attribute feature models including an energy information entropy model to identify raindrop vibration source and a fundamental frequency model to distinguish the construction machine and train or car passing by. Test results show that the design and selection of the feature model are reasonable, and the rate of identification is good.