In recent years， deep learning methods have been widely applied to various disease prediction tasks， even surpassing human experts in some aspects. However， the black box nature of the algorithm limits its clinical application. In this paper， the knowledge representation and reasoning learning and deep learning methods are combined to build an interpretable deep learning model incorporating knowledge representation and reasoning vectors. The model first builds a relationship graph between physical examination indicators and test values according to the normal range of physical examination indicators， and the relationship graph between physical examination indicators and test values is coded through the deep learning model based on knowledge representation and reasoning learning. Then， the patients’ physical examination data are expressed as vectors， which are input into the self-attention mechanism and the classifier constructed by convolutional neural network to realize the disease prediction. When the model is applied to the prediction experiment of diabetes， the accuracy and recall of the model are better than those of the comparative machine learning methods. Compared with the random forest algorithm， the accuracy and recall are also improved by 0.81% and 5.21%， respectively. Experimental results show that the application of knowledge representation and reasoning learning and deep learning technological convergence to diabetes prediction through interpretable methods can achieve the purpose of early detection and auxiliary diagnosis of diabetes.