In recent years, the incidence of Chronic Kidney Disease (CKD) has been increasing, establishing CKD as one of the leading causes of mortality worldwide. Early detection and treatment of CKD can mitigate kidney damage. However, early-stage CKD often presents no overt symptoms, posing challenges for its prediction. Deep learning have shown promising results in the prediction of many chronic diseases. Currently, many scholars have used deep learning methods to predict CKD and achieved good results. However, scholars often only use the patient’s biochemical test data and ignore the chief complaint data when using deep learning to process patient data. We believe that the chief complaint data can provide additional information for model prediction, which is beneficial for the model’s prediction. Therefore, We developed a hybrid model-TG.Net. The attention mechanism module is designed in this model, which can effectively extract information from chief complaint data and improve the model’s ability to predict the five stages of CKD by combining it with biochemical test data. In this study, the model was trained on a clinical dataset and its predictive performance was evaluated using accuracy, precision, recall, and F1 Score. Compared to baselines, TG.Net exhibits better predictive performance, achieving an accuracy of 94%. Subsequently, we explored risk factors associated with CKD and discussed their clinical implications. The TG.Net model demonstrates the potential of deep learning to enhance clinical decision-making and disease prediction.