The work aims to explore a better rapid nondestructive method of detecting the pressed damage of kiwifruit. The hyperspectral imaging system was adopted to obtain hyperspectral images of kiwifruit and the spectra reflectance in damaged region and normal region was extracted. The multi-scatter calibration (MSC) was adopted to preprocess the primary reflectance spectra and principal component analysis was employed to conduct data mining. Then, the effects of Fisher discrimination analysis and simplified K nearest neighbor (SKNN) recognition method in distinguishing the pressed damage of kiwifruit were compared and analyzed. In the spectral range of 710-850 nm and 960-1 030 nm, the average spectra reflectance in damaged region of kiwifruit was obviously different from that in normal region. The first 5 principal components were selected as new variables by PCA from 256 full wavelengths and the detection efficiency of recognition model was improved. The accurate discrimination rates of SKNN and Fisher recognition models for prediction set both reached 93.3%. Only two samples in the prediction set were not distinguished accurately from the confusion matrix of SKNN model. The accurate discrimination rate of SKNN recognition model for calibration set was better than that of Fisher recognition model. SKNN recognition model has better effect in distinguishing pressed damage of kiwifruit.