Changes in the operating conditions of converter transformers can alter the comprehensive vibration modes of the transformer body, resulting in differences in the radiated noise. By deploying relevant sensors around the converter transformer and utilizing artificial intelligence techniques to analyze audible sound signals and array sound signals, non-intrusive acoustic feature monitoring can be conducted on various components such as the large converter transformer body, cooling system, and outlet devices. This approach has the advantages of not affecting the current operating state of power equipment (non-contact with the equipment) and regional monitoring (the sound signals collected by the acoustic monitoring technology form a composite sound field within the region). Multidimensional acoustic features were extracted and processed from the sound signals collected at a certain UHV converter station, revealing that five types of acoustic features (A-weighted sound pressure level, 100 Hz converter transformer fundamental frequency and its harmonic energy ratio, the ratio of odd to even harmonics for 50 Hz, spectral centroid, and spectral entropy) have better autocorrelation and cross-correlation at the same measurement point. These features show significant differences under different operating conditions of the converter transformer, making them suitable for acoustic analysis of the transformer’s operating conditions. This can provide effective references for the intelligent production and maintenance of converter transformers.