Rotating machinery plays a vital role in various industrial sectors. Faced with the strict requirements for the reliability and safety of rotating machinery in the industrial field, this study developed a new acoustic signal analysis method aimed at improving the accuracy and efficiency of rotating machinery fault diagnosis. By combining A-weighting and Short-time Fourier transform (STFT), we propose a time-frequency analysis technology for rotating machinery fault acoustic signals. As a filter that simulates the auditory characteristics of the human ear, A-weighting can reduce the influence of non-critical frequency components during the analysis process, thereby making the analysis results more consistent with the human ear's true perception of sound. Furthermore, the STFT provides us with a powerful tool for capturing and analyzing signal changes in time and frequency. In this study, we first apply A-weighting to the collected acoustic signals to optimize the frequency response range of the signals, and subsequently perform time-frequency analysis on the weighted signals through STFT. The effectiveness of this method is verified through comparative analysis with the traditional STFT method. Experimental results show that our method has significant advantages in reducing background noise interference and improving fault feature identification accuracy. Especially in complex noise environments, A-weighted STFT (ASTFT) shows higher sensitivity and accuracy in identifying subtle fault characteristics. This research not only provides a new acoustic signal analysis method for rotating machinery fault diagnosis, but also greatly improves the naturalness and intuitiveness of the fault diagnosis process by simulating the auditory characteristics of the human ear.