To address the problems of missing and inaccurate prints during the medical infusion bag printing process that impact the medical quality, an intelligent detection and defect classification method based on a support vector machine is proposed for infusion bags. The selected defect classification targets, which are to be classified based on the analysis of the defect characteristics of medical infusion bags during the production process, include the product name offset, product name rotation, and product name stain. These three features, including the location relation between the region of interest and the monitoring region, rotation angle of region of interest and monitoring region, and filling degree are used as the input vectors of the support vector machine to train the classifier. Further, a radial basis function and an one-to-one classification method are used in this experiment. The average operation time and recognition accuracy are considered to be the evaluation criteria for comparing various experiments. The experimental results demonstrate that the recognition accuracy of the proposed method can become 96.7%, satisfying the requirements of commercial production.