A human-machine collaborative classification model based on the deep forest is proposed to solve the problems encountered in the classification of defects in industrial products, such as sample image shortage, insufficient classification precision, and time-consuming model training. For this purpose, sample images are preliminarily identified with deep forest, and their features are extracted by the multi-granularity scanning module and the cascaded forest module. The initial estimation result is thereby obtained, and sample images difficult to identify are separated out. Then, the human-machine collaboration strategy is employed. Specifically, some of the sample images difficult to identify are randomly labeled manually, and the remaining ones are reclassified with the K-nearest neighbor algorithm. The experimental results on the public dataset and the real data collected from the production line indicate that the improved classification model offers performance superior to that of the baseline algorithm on the dataset of industrial product surface defects.