Abstract: Paulownia has usually been an important material for making resonant components of musical instruments, which has a significant influence on the sound quality of musical instruments. This study utilized a generalized regression neural network（GRNN） to develop the sound quality evaluation model of Yueqin based on the vibration performance of the soundboard. In this study, nine Yueqins were fabricated, and a prediction model for the sound quality of Yueqins was proposed based on their sound quality evaluation and the soundboard information of prepared Yueqins. Out of a total of 180 sets of data, 135 sets of data were randomly selected for training and the remaining 45 sets of data were used for validation. A model for evaluating the acoustic quality of Yueqin instruments was established using principal component analysis method and GRNN, and simulation prediction was performed. The results showed that based on the vibration characteristics of the soundboard, the prediction of the Yueqin sound quality can be achieved by using the Matlab simulation, and the accuracy of the prediction can reach 91. 41%. In addition, this study demonstrated that the dynamic elastic modulus, acoustic radiation damping coefficient, elastic modulus, elastic and shear modulus ratio, acoustic impedance, loss tangent angle, and acoustic conversion efficiency of Paulownia wood resonator plates were all key factors influencing the acoustic quality of the finished Yueqin during its preparation.