Pneumoconiosis is a systemic disease mainly caused by diffuse fibrosis of lung tissues caused by long-term inhalation of productive dust during occupational activities and retention in the lungs. China is a country with the largest number of pneumoconiosis patients in the world and a country with the largest number of new cases reported annually. Therefore, it is urgent to strengthen the prevention and treatment of pneumoconiosis. Applying artificial intelligence to pneumoconiosis screening and diagnosis can effectively improve occupational disease diagnosis and radiograph reading efficiency, reduce manual reading errors, and effectively perform quality control. The key to the development of a high-performance artificial intelligence pneumoconiosis digital radiography (DR) reading system (the third category of medical devices supporting artificial intelligence-assisted diagnosis supervised by the National Medical Products Administration) is to establish clear pneumoconiosis artificial intelligence diagnostic standards, and the key support to the establishment of technical foundation framework is the management of data set and the quality control of annotation. By researching and defining the data collection content, screening criteria, and processing flow of pneumoconiosis DR chest radiographs and related information, ideas and methods for data annotation are formed, and quality control of corresponding content and process are performed to lay a solid foundation for the standards of pneumoconiosis artificial intelligence products (models), and to formulate product technical standards and specifications that are rigorous and sound on the ground of medical laws and technical feasibility. To this end, the Chest Imaging and Occupational Diseases Standard Group of the Medical Artificial Intelligence Branch of the Chinese Society of Biomedical Engineering organized domestic experts of public health, occupational medicine and occupational diseases, respiratory diseases, medical imaging, and other fields to discuss on pneumoconiosis chest DR data annotation and quality control. The experts from all parties reached consensus on the collection, screening, quality control, labeling content, labeling methods, labeling rules, labeling process, and quality judgment of pneumoconiosis DR chest radiograph data.