To improve on the poor real-time performance and low accuracy of traditional manual feature extraction and pattern recognition method in the Phase-Sensitive Optical Time-Domain Reflectometric System （φ-OTDR）， a new pattern recognition method based on wavenet is proposed. This method fully analyzes the temporal causality of optical fiber vibration signals through the causal dilated convolutions， and makes the model converge faster by the residual block structure， so as to achieve higher recognition accuracy and efficiency.The experimental results show that， when three signals are recognized， namely， hand tapping， foot stepping， and stick striking， compared with the other two common methods， one-dimensional convolutional neural network structure and long short-term memory structure， the recognition accuracy as high as 99.85% is achieved with the proposed method. And it consumes the least amount of training time， as short as 96 s. Also， its signal detection process takes only 30 ms， which can meet the real-time application's requirement. The proposed pattern recognition method has high accuracy and good real-time performance. It should be of great significance for the application and popularization of φ-OTDR systems in perimeter security and similar areas.