The freshness of egg is an important index to reflect the internal quality. In order to achieve non-destructive detection of freshness, micro fiber spectrometer was used to sample 550～950 nm transmittance spectra of eggs which performed quantitative analysis with haugh unit of eggs. Different pretreatment was combined with partial least squares regression(PLS) and support vector regression(SVR) respectively to find that first derivative combined with SVR predicted better than others through comparison, and it was better to model by SVR than by PLS. In order to improve efficiency and decrease adverse effects of useless information for modeling, the linear dimensionality reduction with principal component analysis (PCA) and the nonlinear dimensionality reduction with locally linear embedding(LLE) were used for the data of first derivative respectively. It indicated that LLE was better than PCA after comparison, and the correlation coefficient of calibration and prediction were 92.2%, 91.1%, and the root mean square error were 7.21, 8.80. The root mean square error of cross validation decreased 0.79.The experimental result illustrated that the nonlinear model of LLE combined with SVR improved predictive performance of egg freshness. It is feasible for the detection of visible/near-infrared spectrum of egg freshness to apply this method.