Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class-imbalance problem in the credit default dataset cannot be ignored, where the numbers of credit default cases are usually much smaller than the number of non-default ones. To address the class-imbalance problem, we proposed a novel CT-XGBoost model, which adds to XGBoost with two algorithm-level methods for class imbalance, including the cost-sensitive strategy and threshold method. Based on the credit default dataset consisting of energy corporates in western China, which suffers from the class-imbalance problem, the CT-XGBoost model achieves better performance than the conventional models. The results indicate that the proposed model can efficiently alleviate the inherent class-imbalance problem in the credit default dataset. Moreover, we analyze how the prediction performance is influenced by different parameter settings in the cost-sensitive strategy and threshold method. This study can help market investors and regulators precisely assess the credit risk in the energy industry and provides theoretical guidance to solving the class-imbalance problem in credit default prediction.

Named Entity Recognition (NER) aims to identify entities with specific meanings and their boundaries in natural language texts. Due to the differences between Chinese and English language families, Chinese NER faces challenges such as ambiguous word boundary delineation and semantic diversity. Previous studies on Chinese NER have focused on character and lexical information, neglecting the unique feature of Chinese—pinyin information. In this paper, we propose CPL-NER, which combines multiple feature information of Chinese characters as embedding to enhance the semantic representation by introducing pinyin and dictionary information. For Chinese named entity recognition, pinyin information of Chinese characters helps to resolve the polyphonic phenomenon, while dictionary information aids in addressing word segmentation ambiguities. Additionally, we innovatively designed the Pinyin-Lexicon Cross-Attention Mechanism (PLCA), which calculates attention scores between various embeddings. This mechanism deeply integrates character, pinyin, and lexicon embeddings, generating character sequences enriched with semantic information. Finally, BiLSTM-CRF is employed for sequence modeling. Through this design, we can more comprehensively capture semantic features in Chinese text, improving the model’s ability to handle polyphonic characters and word segmentation ambiguities, thereby enhancing the recognition performance of Chinese named entities. We conducted experiments on four standard Chinese NER benchmark datasets, and the results show that our method outperforms most baselines, demonstrating the effectiveness of our proposed model.

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the “dual-carbon” goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system.