［Objective ］ In order to improve the accuracy of egg appearance quality detection,an egg appearance quality detection model based on CNN -SVM model was established.［Methods ］ Combined with the adaptive feature extraction capability of CNN and the super -generalization classification capability of SVM,the features of fully connected layers were extracted by six -layer convolutional neural network structure processing,and the CNN -SVM hybrid model was adopted,instead of the traditional CNN + softmax,an egg appearance quality detection method based on CNN -SVM model was proposed.［Results］ Compared with SVM model,CNN model and KNN model,CNN -SVM model had better performance in accuracy,precision,recall and F1 score,which were 97.97%,98.10%,98.10% and 98.00% respectively.KNN model had the lowest accuracy in egg appearance quality detection,and its accuracy,precision,recall and F1 fraction are 77.46%,79.44%,76.75% and 76.90%,respectively.［Conclusion ］ The CNN -SVM model has strong robustness and anti -noise ability,which can effectively improve the accuracy and applicability of egg appearance quality detection..