This article applied the duck egg internal visible light image information (H and I) to characterize the freshness of duck egg. The visible light image analysis technology was applied to obtain the duck egg heart color parameters. The Hafu unit was used as the confirmation index of egg freshness through experiment, then established the forecast model based on SVM (support vector machines) which was developed using the egg heart color parameters to predicate the egg freshness. The model indicated that when we choose the SVM type as epsilon-SVR, the nuclear function as RBF ,the model characteristic parameter C=27, σ=23 ,then the model forecast effect is the best. The model forecast effect parameters RMSEC reached 0.9520, and the EMSEP reached 0.4205. The predicted value of egg freshness and the practical value has good linear correlation and predicted value has a large covering and searching ability on the practical value. This model showed better stability and confidence interval compared with the ordinary linear method. Through the comparison of SVM and neural network recognition results we can get the conclusions that SVM has a better duck-egg freshness recognition performance than ANN by egg heart color's analysis(SVM reach to 98.92%>ANN reach to 93.77%).