Abstract: To solve several problems in current noise quality evaluation, such as low accuracy in using only Aweighted sound pressure level to determine the noise quality of industrial sewing machines, low model generalization due to using only single random partitioning dataset for modeling, and failure to adequately reflect the binaural hearing characteristics of human due to using only a single-channel data, a noise quality evaluation method for industrial sewing machines is proposed in terms of objective parameters of sound quality. Firstly, the objective parameters, including sound pressure level, A-weighted sound pressure level, loudness, sharpness, fluctuation strength, roughness and tonality, are calculated. Secondly, the subjective evaluation test is carried out by using the sorting method based reference grade scoring method. Finally, the left-ear and right-ear independent prediction model and the binaural fusion prediction model for the noise quality evaluation of industrial sewing machines are established by Monte Carlo simulation and multiple linear regression. The results show that: the model obtained by using Monte Carlo simulation has the largest statistical probability, and has less randomness and greater generalization compared with the model formed by single random partitioning dataset; the test accuracies of the left and right ear independent models are 93.56% and 92.56%, respectively, while 94.53% for the binaural fusion model, which indicates that the prediction results of the binaural fusion model are better matched with the human subjective evaluation results.