Over the past century, the Communist Party of China (CPC) has transformed China from extreme poverty to prosperity, leading the country into modernization. Utilizing descriptive statistics, the Latent Dirichlet Allocation (LDA) topic model, and word co-occurrence networks, this paper systematically explores the development and governing philosophy articulated in the CPC’s discourse, as presented in the official reports of 20 Party Congresses from 1921 to 2023, in order to comprehend the pathways of China’s rapid development. For better understanding through visualization, a thematic evolution chart is constructed to display the CPC’s ideological development, and aword co-occurrence network is established to illustrate the changes in terminology over time. The analysis reveals distinct characteristics of the CPC’s development and governance across different phases, specifically shifting from a focus on revolutionary ideals and class struggle in the early stages to an emphasis on economic reforms and modernization in recent stages. Such kind of works not only help to catch up the core concepts and working endeavors during the different period of development, but also highlight the significance of analyzing the political documents from a systemic perspective.

The main contents of this work were previously presented at the 22nd International Symposium on Knowledge and Systems Sciences (KSS2023) held in Guangzhou during December 2–3, 2023.

As the national Chinese medicine market develops, Chinese medicinal materials price index (CMMPI) trend is worthy of attention. Predicting future CMMPI trend plays a significant role in risk prevention, cultivation, and trade for farmers and investors. This study aims to design a high-precision model to predict the future trend of the CMMPI. The model incorporates environmental factors such as weather conditions and air quality that have a greater impact on the growth of Chinese medical plants and the supply of Chinese medicinal materials market. Specifically, we collected multi-source heterogeneous data, including weather data, air quality data, and historical CMMPI data, to construct informative features. Additionally, we proposed a feature selection method based on the genetic algorithm and XGBoost to select features. Finally, we transferred the selected features to the bidirectional GRU deep learning to realize the accurate prediction of the CMMPI trend. We collected 46 CMMPI datasets to test the proposed model. The results show that the proposed model obtained more superior prediction compared to the state-of-the-art methods, and specialized in predicting long-term goal (90 days). Taking the Yunnan and Tibet origin index as examples, the experiment results also show the weather and air quality data can improve the prediction performance, as these factors are known to influence the growth and market supply of Chinese medicinal materials.

Due to the increasing importance of online product reviews, how to accurately identify fake reviews has become an issue of concern to enterprises and consumers. The contextual features encapsulate the semantic information of review, while the behavioral features reflect the behavioral patterns of reviewers. However, an appropriate method to integrate contextual and behavioral features is a challenging task, hence an end-to-end model based on Weighted Fusion of Contextual Features and Reviewer Behaviors (WF-CFRB) for fake review detection is proposed. Firstly, the categories of average cosine similarity and the corpus of review are jointly fed into BERT to obtain contextual feature vectors. Then, the underlying patterns of the reviewer behaviors are extracted by CNN to construct behavioral feature vectors. Finally, a weighted fusion method is adopted to fuse contextual and behavior features for fake review detection. WF-CFRB and each component are evaluated on YELP dataset. WF-CFRB achieves F1 score of 81.31% and AUC score of 81.27%, and it also outperforms the other baseline models in terms of accuracy and recall. Compared with the original BERT model, the experimental results indicate that cosine similarity provides BERT with more information, which is useful to construct the contextual feature vectors. Through the weighted fusion of contextual and behavioral features, WF-CFRB yields excellent performance on fake review detection, which is particularly suitable for scenarios where behavioral features can be captured.

Machine learning has been widely used in the field of credit scoring due to their excellent predictive performance, but opacity hinders the further application of more accurate complex machine learning models. We propose a credit score explainable framework that integrates multiple models, uses the XGBoost algorithm to predict credit scores, and then uses multiple explanation algorithms and K-means to enhance the accuracy and explainability of credit scores. The paper uses data sets from public websites to test model performance. The results show that the credit scoring model can simultaneously achieve the two goals of accurate prediction and stable explanation, making the credit scoring process easy to understand.