The work aims to detect the defects of drug packaging with aluminum plastic bubble cap by means of machine vision technology, regarding such packaging defects as low efficiency, high cost and poor stability in the manual detection of drug with aluminum plastic bubble cap. A defect detection algorithm framework consisting of a fast robust feature SURF extraction algorithm, a BOW algorithm and a one-class support vector machine were adopted and the defect detection system for the drug packaging with aluminum plastic bubble cap was developed. Based on the experimental platform, 280 images of drug with aluminum plastic bubble cap were obtained, and the defects of 180 images were detected in the proposed method. The experimental results showed that, when the threshold was 1,900, the number of visual words was 120 and the penalty factor was 0.9, the accuracy of the proposed method was 99.4%. The proposed method improves the accuracy and stability of the defect detection of drug packaging with aluminum plastic bubble cap.