Based on the shortcomings of the traditional detection methods for meat freshness, such as time-consuming, laborious, low efficiency, loss and other defects, and put forward using hyperspectral imaging (HSI) technology to predict cooked beef freshness index of volatile basic nitrogen (TVB-N) content. Firstly, the hyperspectral data of cooked beef samples were obtained by HSI system, and the black and white correction was carried out. And then, the hyperspectral data was preprocessed using the moving average smoothing and the multiple scattering corrections. Finally, the support vector regression (SVR) method was used to establish the prediction model of TVB-N content based on the whole spectral feature, single spectral feature, single texture feature and PCA fusion feature. The experimental results showed that the Average Predicting Accuracy (APA) for the TVB-N content index of freshness could reach 85.13% by SVR model with PCA fusion feature, also showed that hyperspectral imaging technology combined with information fusion technology could improve the prediction accuracy of the model.