Aiming at the problem of noise residual in Non-negative Matrix Factorization (NMF) speech enhancement algorithm in low Signal-to-Noise Ratio (SNR) unsteady environment, a Perceptual Masking-based reconstructed NMF (PM-RNMF) single-channel speech enhancement algorithm was proposed. Firstly, psychoacoustic masking features were applied to NMF speech enhancement algorithms. Secondly, different masking thresholds were used for different frequencies to establish an adaptive perceptual masking gain function, and the residual noise energy and speech distortion energy were constrained by the thresholds. Finally, Speech Presence Probability (SPP) was combined to realize perceptual gain correction, the NMF algorithm was reconstructed and a new objective function was established. The simulation results show that under three kinds of unsteady noise environments with different SNR, the average Perceptual Evaluation of Speech Quality (PESQ) of PM-RNMF algorithm is improved by 0.767, 0.474 and 0.162 respectively and the average Signal-to-Distortion Ratio (SDR) is increased by 2.785, 1.197 and 0.948 respectively compared with NMF, RNMF (Reconstructive NMF) and PM-DNN (Perceptual Masking-Deep Neural Network) algorithms. Experimental results show that PM-RNMF has better noise reduction effect in both low frequency and high frequency.