Masks protect respiratory health in the coal, oil, and gas industries. However, prolonged exposure to high humidity inside masks can cause discomfort and increase the risk of respiratory diseases. To address the issue, in this work, a porous polymer humidity sensor suitable for monitoring respiratory changes in high humidity environment is prepared and integrated into the power air-purifying mask. Combined with the transfer learning algorithm, the problem with respiratory resistance caused by delayed air supply due to signal processing in the traditional power air-purifying mask is overcome, and the respiratory signal is effectively predicted in advance, so as to achieve real-time on-demand air supply. The brief process is as follows: A porous polymer humidity sensor with fast response/recovery times (2.94/4.86 s) at 95% relative humidity (RH) is developed for monitoring respiratory rate changes in high humidity environments. By integrating this sensor with a powered air-purifying system and employing a transfer learning algorithm, the system predicts respiratory signals and adjusts the air supply in real-time. This reduces mask humidity from 95% RH to 40–50% RH in 1.8 s, ensuring comfortable, low-resistance breathing for workers.This work will be conductive to the development of comfortable poweredair-purifying respirators with low resistance and humidity.