Abstract: Seed maturity has a great influence on the quality of wine, and it needs to be observed and judged by the naked eye by experts who have been trained for a long time. In order to change the way of traditional artificial experience judgment, a grape seed image classification and recognition algorithm based on Gabor wavelet feature extraction and deep neural network is proposed to achieve efficient and accurate classification and recognition. First, the background difference method is used to segment the interest target in the background image, thereby completing the image preprocessing. Then, the improved Gabor wavelet feature extraction makes the Gabor filtered image have more detailed texture information. Finally, the deep convolutional neural network and the extracted texture feature information are combined to classify. The experimental results show that the recognition of grape seed maturity based on machine learning is feasible. The proposed image classification accuracy exhibits a certain improvement compared with other similar classification algorithms.