Credit risk is one of the main financial risks which commercial banks are faced with, while traditional credit scoring methods cannot effectively make use of the existing feature learning methods, resulting in low prediction accuracy. To solve this problem, an enhanced multi-dimensional and multi-grained cascade forest method was proposed to build credit scoring model, with the use of the idea of residual learning, the multi-dimensional and multi-grained cascade residual Forest (grcForest) model was built, which greatly increased the extracted features. Besides, the multi-dimensional multi-grained scanning was used to extract features of the raw data as many as possible, which improved the efficiency of feature extraction. The proposed model was compared with the existing statistical and machine learning methods on four credit scoring datasets, and evaluated by Area Under Curve (AUC) and accuracy. The AUC of the proposed model was 1.13% and 1.44% higher then that of the Light Gradient Boosting Machine (LightGBM) and the eXtreme Gradient Boosting (XGBoost). Experimental results show that the proposed model performs best in the prediction.

Aspect-based sentiment analysis tries to estimate different emotional tendencies expressed in different aspects of a sentence. Aiming at the problem that the existing network model based on Recurrent Neural Network (RNN) combined with attention mechanism has too many training parameters and lacks explanation of related syntax constraints and long distance word dependence mechanism, a self-attention gated graph convolutional network was proposed, namely MSAGCN. First, the multi-headed self-attention mechanism was used to encode context words and targets, thus capturing semantic associations within the sentence. Then, a graph convolutional network was established on the sentence's dependency tree to obtain syntactic information and word dependencies. Finally, the sentiment of the specific target was obtained through the GTRU (Gated Tanh-ReLU Unit). Compared with the baseline model, the proposed model has the accuracy and F1 improved by 1%-3.3% and 1.4%-6.3% respectively. At the same time, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model was also applied to the current task to further improve the model effect. Experimental results verify that the proposed model can better grasp the emotional tendencies of user reviews.