During operation， power transformers emit vibration signals continuously， which contain many pulses and fluctuations caused by mechanical faults. These signals serve as the primary data source for assessing the operating condition of power transformers. To address the issues of low diagnostic accuracy， poor real-time performance， and inability to handle multi-fault scenarios effectively in the traditional power transformer fault diagnosis techniques， the voiceprint characteristics of internal vibration acoustic signals in power transformers were analyzed， and a fault diagnosis method integrating dual-threshold preliminary screening and edge-level dynamic graph convolution was constructed on the basis of acoustic features. First， a normal/fault signal classification module was proposed to assess the real-time operating condition of power transformers using acoustic short-time energy and zero-crossing rate. Second， an Edge-Level Dynamic Graph Convolutional Network （ELDGCN） was designed to learn spatial correlations among voiceprint signals adaptively by optimizing edge weights. Finally， experiments were conducted under real-world power transformer scenarios. The results show that the proposed method improves the fault identification efficiency significantly， with the identification rate for datasets containing mixed faults reaching 98.17%.