Occupational diseases present a significant global challenge, affecting a vast number of workers. Accurate prediction of occupational disease incidence is crucial for effective prevention and control measures. Although deep learning methods have recently emerged as promising tools for disease forecasting, existing research often focuses solely on patient body parameters and disease symptoms, potentially overlooking vital diagnostic information. Addressing this gap, our study introduces a Deep Graph Convolutional Neural Network (DGCNN) designed to detect occupational diseases by utilizing demographic information, work environment data, and the intricate relationships between these data points. Experimental results demonstrate that our DGCNN method surpasses other state-of-the-art methods, achieving high performance with an Area Under the Curve (AUC) of 96.2%, an accuracy of 98.7%, and an F1-score of 75.2% on the testing set. This study not only highlights the effectiveness of DGCNNs in occupational disease prediction but also underscores the value of integrating diverse data types for comprehensive disease diagnosis.