The work aims to study a method of defect identification for blister packaging medicine based on multi-feature construction and integrated classifier in view of the problem thatmany defective blister packaging medicine products appear in pharmaceutical production process. Two different classifier algorithms were integrated to predict the categories of medicine image respectively, and the combined judgment function was designed to make joint decision on the two predicted output value to obtain the final classification result. In the first classifier model, the image was transformed into HSV color space to segment the blister region and pill region, and feature engineering was carried out and multiple feature parameters were extracted to construct BP neural network classification algorithm to predict the given medicine categories. In the second classifier model, multi-layer convolutional neural network was used to extract the image features instead of the traditional algorithm and output the prediction of medicine image categories. According to the performance of the two classifiers, the algorithm was integrated to the final integrated classifier. The experimental result showed that the data set was tested in classification and identification with this integrated classification model and the accuracy rate was more than 97%. The integrated classification model not only improves the identification accuracy of a single classifier, but also has better stability. This method has achieved prominent classification effect and has high applicability.