The bolt-fastening detection of transmission towers is critical to the safety of high-voltage power grids. Traditional detection methods are often risky it needs manual detection high on transmission towers. What’s more, UAV detection fails to live up to our expectation affected by multiple external factors. Therefore, this study proposes a bolt-fastening detection method for transmission towers based on Gated Recurrent Unit (GRU) networks. Specifically, the vibration sensor and sensor analyzer are used to construct a work flow for collecting acoustic wave data of transmission towers, and then the Linear Predictive Cepstral Coefficients (LPCCs) of acoustic wave data in training samples are extracted to form feature vectors. The classification model of GRU networks is trained to predict unknown fastened acoustic wave samples. As a result, this method is practical. The application of this algorithm can avoid the much manpower of traditional ones and is superior to them in bolt-fastening detection of transmission towers.