Deep Neural Networks (DNNs) have powerful learning abilities on high-rank and non-linear features, and thus have been applied to various fields, exhibiting higher discrimination performance than conventional methods. However, their applications in enterprise credit rating tasks are rare, as most DNNs employ the “end-to-end” learning paradigm, producing high-rank representations of objects or predictive results without any explanations. This “black box” approach makes it difficult for users in the financial industry to understand how these predictive results are generated, or what correlations exist with the raw inputs, leading to a lack of trust to the predictions. To address this issue, this paper proposes a novel network to explicitly model the enterprise credit rating problem using DNNs and attention mechanisms, allowing for explainable enterprise credit ratings. Experiments conducted on real-world enterprise datasets show that the proposed approach achieves higher performance than conventional methods, while also providing insights into individual rating results and the reliability of model training. The code is provided on .