Objective: To classify banana ripeness quickly and accurately. Methods: Collect the bananas images of different maturity and establish gallery, using a variety of different neural networks as a classifier, banana feature extracting by migration study classifying banana six maturity level, access to the most suitable for banana maturity classification network model, network model, based on the improved and easily banana maturity real-time detection interface design, Finally, the feasibility and practicability of the model were verified. Results: AlexNet model was most suitable for banana maturity classification with the highest accuracy of 95.56%. AlexNet model was improved by modifying its full-connection layer structure, and the model accuracy was further improved by 1.11%. Conclusion: AlexNet model can quickly and accurately identify and classify bananas of different maturity.