Aiming at the problem of difficulty in improving the classification accuracy of industrial fault detection caused by its limited number of labeled training samples which would consume a significant amount of manpower, which a large number of unlabeled samples containing rich information are not fully utilized, this paper puts forward a semi-supervised classification model of combining active learning (AL) and the optimum-path forest (OPF). Firstly, the high-value samples are selected in sorting batch mode according to the value of samples that are comprehensively measured based on BvSB and cosine similarity criterion, and the value of each sample is obtained to expand the initial labeled sample set. Secondly, semi-supervised label propagation is achieved by constructing the optimumpath forest. Finally, the experimental verification was carried out using laboratory collected pipe condition datasets. The experimental results show that the method can achieve an overall recognition accuracy of 96. 68% when the number of labeled samples is 10%. Compared with active learning methods in one-by-one sampling mode and semi-supervised methods that extract global structural information of training samples based on distance metrics, the proposed method has higher Recall value and F1-score value.