Abstract: Crispness is one of the most important internal indexes of Korla pear. However, there are serious differences in crispness in the Korla pears in recent years, due to the production areas, different varieties, and maturity changes. Although firmness has been widely used to evaluate the internal quality of fruit, there is quite different from the crispness index. The crispness can be attributed to the comprehensive perception of the contact pressure from teeth and sound from ears. The current evaluation of fruit crispness depends mainly on the sensory panel, where the sensory evaluators are easily tired and disturbed by the external environment during the evaluation process. Therefore, it is necessary for an objective evaluation of the crispness of the pear. In this study, a systematic evaluation was made for the crispness fusing features extracted from the mechanical-acoustic response of pears under rupture. Specifically, 50 pears were taken every 12 days and then refrigerated at -2~0°C. At the same time, the instrument measurement and sensory evaluation were performed on all samples after 60 days. The texture analyzer and microphone were combined to collect the mechanical-acoustic signals during the rupture of pear flesh. The jaggedness of the signal curves was extracted from the multiple features using the peak method, Fourier power spectrum, and apparent fractal dimensions. Among them, 29 jaggedness parameters were extracted from the force signals, and 14 jaggedness parameters from the acoustic signals. Firstly, Pearson’s correlation analysis was used to determine the relationship between 43 jaggedness parameters and sensory crispness of Korla pear flesh. The results showed that there was no significant difference in the sensory crispness in the five jaggedness parameters of the force curves and three jaggedness parameters of the acoustic curves. Specifically, there was a strong correlation (|R|>0.8) between the sensory crispness and low strain stiffness MLS, Young's modulus MYM, Richardson of the mechanical signature's apparent fractal dimension MDR, Kolmogorov of the mechanical signature's apparent fractal dimension MDKL, which were 0.876, 0.882, 0.859, and 0.886, respectively. The remaining 35 parameters were used as the evaluation indexes of pear flesh crispness. Then, the selection of jaggedness parameters was subjected to the autocorrelation analysis. Specifically, most of the parameters presented different degrees of correlation, where 34 parameters were strongly correlated. Subsequently, the principal component analysis was conducted to reduce the serious information redundancy. The first eight principal components were then determined to represent the jaggedness parameter information of force acoustic response signals, representing 85.58% of the total variability, according to the variance contribution rates of principal components analysis. Finally, the multiple linear regression analysis was carried out on the selected principal components and sensory crispness. A prediction model was constructed for the pear flesh crispness. All the coefficients of the model passed the T test (P<0.05). The correlation coefficient for the calibration set (Rc2) and validation set (Rp2) of the model were 0.862 and 0.889, respectively. The root mean square error of the calibration set (RMSEC) and validation set (RMSEP) were 0.588 and 0.551, respectively. Consequently, the improved model was stable and reliable to detect the crispness of the pear flesh. Therefore, the crispness model of pears can be expected to extract the multiple characteristic parameters in the process of picking, storing, and selling. This finding can provide the theoretical research basis for the instrument detection of the crispness of fruit.