Over the past decade, a large body of research has underscored the intricate link between the occurrence of various complex diseases in humans and microbial communities, with microorganisms exerting significant impacts on human health through modulating biological processes. Biological experiments excel in accuracy for identifying disease-related microbes but suffer from prolonged timelines and excessive costs. The development of predictive models capable of accurately identifying disease-related microbes can effectively mitigate labor and time costs. In our investigation, a deep learning model named GRNMDA was developed for predicting human Microbe-Disease Association (MDA) by fully leveraging diverse biological data to construct the features of microbial and disease entities. Initially, our model calculated the functional similarities and Gaussian Interaction Profile (GIP) kernel similarities for each microbe and disease. To create a holistic similarity matrix for microbes and diseases, these features underwent fusion. Afterward, the feature representation of each microbe-disease pair was captured using a Graph Attention Auto-Encoder. Subsequently, based on the idea of Positive Unlabeled Learning (PU Learning), spectral clustering and the Light Gradient Boosting Machine (LightGBM) algorithm were combined to select reliable negative samples. Finally, the extracted MDA features, along with the chosen negative samples, were used as input data, and a modified residual network was constructed to predict potential MDAs. Using the model on HMDAD, extensive tests showed remarkable results: AUC of 96.47% and accuracy of 98.87%. Compared to baseline models, improvements were up to 1.56% and 6.97%, respectively. The model’s efficacy and dependability were further demonstrated by case studies on conditions including colorectal cancer.