Abstract: Foreign objects mixed in during coal mining may cause accidents such as blockage or even tearing of conveyor belt connections. Most existing machine learning algorithms for coal flow foreign objects use supervised learning to automatically recoginze item categories. However, in real industrial and mining scenarios, the scarcity of abnormal samples leads to problems of serious imbalanced sample distribution and significant features lost in the modeling dataset. In order to solve the above problems, a coal flow foreign object intelligent detection method based on dual-attention Skip-GANomaly (DA-GANomaly) is proposed. This method adopts a semi supervised learning approach, which only requires normal samples to complete the training of the foreign object detection model, effectively solving the problems of low recognition accuracy and poor robustness caused by imbalanced sample distribution. On the basis of Skip-GANomaly, a dual attention mechanism is introduced to enhance the information exchange between the encoder and decoder and suppress irrelevant features and noise. It highlights the interesting features that are conducive to distinguishing abnormal samples, and further improves the accuracy of model classification. The experimental results show that the classification accuracy of the DA-GANomaly model is 79.5%, the recall rate is 83.2%, and the area under the precision recall curve (AUPRC) is 85.1%. Compared with 5 classic anomaly detection models such as AnoGAN, the DA-GANomaly model has the best overall performance.