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In view of the current single detection index of the jujube grading device, and it is difficult to realize comprehensive judgement of external quality, thus a dry Hami jujube external quality detection system based on deep learning and image processing was developed. Firstly, crack, bird peck and mildew defects were detected by deep learning image classification. To overcome the problems of large computation, high complexity and information loss of current residual network, an improved image classification method based on deep residual network was proposed. Secondly, according to the grade difference between size and texture quantity, a threshold detection method was proposed, which can realize the detection of size and fold by extracting the features of area, perimeter, fitting circle radius and texture quantity of dried Hami jujube image. The test results showed that the accuracy of models for detecting defect, size and fold were 97.25%, 93.75% and 93.75%, respectively. Combining three external quality indexes, the detection performance of the system was verified by online image acquisition. After testing, the comprehensive accuracy for detecting external quality was 93.13%, which can initially meet the production requirements of online detection equipment for dried Hami jujube quality. The reearch result can provide theoretical basis and technical reference for the development of rapid nondestructive detection system of dried fruit quality.