Aiming at the disadvantages of traditional coding detection methods such as large amount of calculation, insignificant character area positioning, and low recognition accuracy, a machine vision-based flexible packaging bag coding defect detection method is proposed. With the coded image on the flexible packaging bag as the research object, the image is preprocessed by filtering and noise suppression, threshold processing and other technologies. YOLO-V3 network model is used to locate character area, and threshold and non-maximum suppression algorithm is used to improve the significance of coding area positioning. By improving the AlexNet network structure and methods such as multi-feature fusion operations, more abundant image convolution features are obtained and the accuracy of coding defect recognition is improved. By comparing the detection method of YOLO-V3 jointly-improved AlexNet with the traditional coding detection method, it is found that the classification accuracy of the designed defect detection method is 99.39%. The machine vision-based flexible packaging bag coding defect detection method has certain advantages in the amount of model calculation, the significance of character area positioning, and the accuracy of character recognition, and it can also effectively solve the problem of string overall recognition.