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A semiconductor laser generator with 650nm wavelength and power of 13.25mW was used to irradiate the surface of “Jiro” persimmon during the storage and the characteristic laser refractive image was collected by a CCD camera. Through the midpoint subdivision method, the image region segmentation threshold was determined. Then, the image segmentation of the pixel size parameters, regional information entropy of the gray value as well as the standard deviation of gray value was calculated. The system parameters above were chosen as the parameters set. In order to get more compact model, the principal component analysis (PCA) was taken on the parameters set in the forecasting course of “Jiro” persimmon’s soluble solids. Through the analysis, the most important laser image parameters were obtained for the contribution in forecasting the soluble solids content of “Jiro” persimmon. An improved SVM regression model was designed to forecast the “Jiro” persimmons soluble solids content with the laser image parameters obtained by PCA. Both model performance parameters and verification experiments showed that the model had good stability and accuracy with the SVM related index R of 0.9905 and the average prediction accuracy was 94.1%.