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Effective credit risk assessment of heavy-polluting enterprises can achieve a balance between environmental and economic benefits. It requires the consideration of risk indicators for both the carbon information dimension and the compliance dimension. However, as the feature dimensions of the model continue to increase, so does the irrelevant feature or noise. Therefore, we investigate the use of non-integers for regularization from high-dimensional data under the conditions of a large number of irrelevant features. In this paper, a novel Wide-<i>ℓ_p Penalty</i> and Deep Learning (WPDL) method for credit risk assessment is proposed, which could provide a sparse solution. The Wide-<i>ℓ_p Penalty</i> component allows feature selection using a linear model with an <i>ℓ_p Penalty</i> regularization mechanism, where 0 < <i>p</i> ≤ 2. The deep component is a DNN that can generalize indicator features from the credit risk data. The experimental results show that the minimum prediction error occurs at a non-integer <i>ℓ_p Penalty</i>. Furthermore, the WPDL outperforms other models such as KNN, DT, RF, SVM, MLP, DNN, Gradient Boosting, and Bagging.