Disclosed herein is a method for RNN-based noise reduction in a real-time conference, comprising: performing frame-and-window for a speech signal to obtain a logarithmic spectrum of the speech signal, and placing the logarithmic spectrum into the RNN model to determine a noise reduction suppression coefficient, and then obtaining the denoised speech signal by applying the noise reduction suppression coefficient to the logarithmic spectrum of the original signal, thereby achieving utilization of the RNN noise reduction method in real-time conferences. In the present disclosure, when inputting the RNN model for estimation, only the logarithmic spectrum of the current frame needs to be inputted. The RNN model of the present disclosure has few requirements on inputted information, without performing huge preprocessing on the received speech signal, which in turn reduces computation burden, increases response speed, and enhances real-time performance.