Railway transportation safety provides guarantees for ensuring the safety of passengers’ lives and property and the smooth transportation of goods. The current status and future directions of railway transportation safety were studied in China through two bibliometric analysis methods. The results show that railway transportation safety research can be divided into passive constraint stage, active constraint stage and active management improvement stage. The research focuses on the safety operation protection system, grassroots station safety management, prediction and evaluation of transportation safety accidents, and factors affecting transportation safety accidents. The future of railway transportation safety should focus on the integration of information technology for safety management, intelligent technology for train operation safety, and dynamic monitoring of the external environment of railways.

In the financial business, effective risk avoidance and mitigation are perennial themes. In the face of the international economic and financial environment has become more complex and severe, extreme events caused by the tail risk brought about by the harm is very strong, China’s banking industry risk prevention and control work will face new challenges. Therefore, under the current globalization and complex and severe economic and financial environment, it is of great practical significance to improve the prediction ability of yield risks of commercial banks and take timely measures to prevent and resolve the risks. Selecting the monthly data of China’s A-share listed commercial banks from January 2013 to December 2022, a nonparametric Expectile Regression Forest(ERF) risk prediction model based on the Bagging algorithm to measure the tail risk of China’s commercial banks, and simultaneously incorporates the bank leverage ratio, asset size, economic policy uncertainty, financial market volatility and liquidity into the model at the same time were constructed, and then the mean absolute error and root mean square error of the training set and test set data under different models and risk levels respectively was calculateed. Finally, the results were compared with the traditional expected quantile regression(ER) model and Expectile Regression Tree(ERT) model to determine the respective predictive performance. The results show that the ERF model has outstanding performance in measuring the tail risk of commercial banks, and the estimation and prediction ability of the ERF model is significantly better than that of the ERT and ER models under different levels of risk. Further analysis reveals that the smallest and the largest errors in the prediction of tail risk of the four major types of commercial banks are those of the national large-scale commercial banks and the local rural commercial banks, respectively, and the error values of the joint-stock commercial banks and the local urban commercial banks are comparable. The error values of joint-stock commercial banks and local urban commercial banks are comparable.

The tourism industry is undergoing a profound transformation, with tourists’ demands and preferences showing a diversified trend. Accurately identifying tourists’ preferences and satisfaction levels is of crucial importance. Taking the Longji Terraced Fields Scenic Area in Guilin as a case study, deep learning technology and cluster analysis methods were employed to extract the themes of tourists’ service preferences from online reviews and identify groups of tourists with similar preferences. Combined with questionnaire survey data, the service satisfaction was quantitatively evaluated. The research finds that tourists’ service preferences cover accommodation, catering, transportation and information services. The improvement space for information services and accommodation services is the largest, while transportation services and catering services also have room for improvement. Based on this, corresponding suggestions are put forward.

Forecasting the stock market is a difficult and intricate task, as price series often display traits like significant noise, nonlinearity and non-stationarity. In order to improve the accuracy of predictions, a new method that combined the fuzzy C-means (FCM) clustering algorithm to identify and utilize local trend features in stock price prediction sequences was proposed. In the analysis, key market data of stocks, including opening price, highest price, lowest price, closing price, trading volume, and trading amount, was comprehensively considered as input features for the prediction model. Through experiments, an empirical analysis was conducted to compare the impact of different sliding window sizes (16, 32, 64) on the model’s predictive capability. It is found that the FCM-LSTM-Transformer method, which integrates FCM clustering with the LSTM-Transformer combination model, achieves higher prediction accuracy than both the standalone deep learning models and the LSTM-Transformer combination model. The evaluation metrics MAE, MAPE, MSE and RMSE reach their minimum errors, and the coefficient of determination R² improved by 2.75%, 2.4% and 2.19%, respectively. These results indicate that the proposed model has a significant advantage in handling the complexity of stock market data.

The impact of economic policy uncertainty on M&A performance was examined. Taking the M&A data of A-share listed companies from 2007 to 2023 as the study sample, it is found that the impact is negative and statistically significant, and the moderating effect factor investor sentiment exacerbates this impact. Further analysis shows that during economic upturns, economic policy uncertainty has the greater significant negative effect on M&A performance compared with the economic downturns. The similar condition happens in the enterprises with non-concentrated institutional investor holdings. But economic policy uncertainty does not seem to influence the M&A performance of the enterprises with green M&A type or located in first-tier or new first-tier cities.

Digital economy has become an irreversible development trend, and the national level actively advocates the cluster development of digital economy related industries. Given that Guangdong Province occupies a leading position in the development of digital economy in China, it is of profound practical significance to explore the specific impact and mechanism of different levels of industrial agglomeration and knowledge spillover effect on the innovation ability of digital economy enterprises in Guangdong Province. Based on the digital economy patent data of listed enterprises and the economic indicators of prefecture-level cities in Guangdong Province,threshold regression model and spatial Durbin model were applied to conduct in-depth analysis. The research conclusions are as follows. The agglomeration level of digital economy industry shows a significant single threshold effect on the innovation ability of enterprises, and the relationship between the two shows an inverted “U” shape. The innovation capability of digital economy enterprises presents a distinct spatial agglomeration characteristic, and the knowledge spillover effect has a significant positive promoting effect on the improvement of enterprise innovation capability.

It is of great practical significance to explore how B2B enterprises select suitable third-party logistics providers in the context of e-commerce. A multi-criteria decision-making model was established to evaluate and compare the comprehensive competitiveness of multiple third-party logistics providers by using the fuzzy analytic hierarchy process. The model considers key factors such as service quality, cost optimization, flexibility, technical capabilities and corporate responsibility. Subsequently, the selection process is further optimized by combining stochastic integer programming and robust optimization methods to ensure that business needs are met while maximizing cost-effectiveness. Finally, the effectiveness of the proposed method is verified through case studies, providing scientific decision-making support for B2B enterprises in selecting third-party logistics in an e-commerce environment.

Land-sea integration is a new concept derived from the concepts of “land sea coordination”. It is a strategic guiding ideology for China to handle the relationship between ocean and land development, and is of great significance for promoting coordinated development between land and sea. As the closest super large city to the deep waters of the South China Sea in China, Shenzhen has not yet been able to effectively transform its location advantages and land technology industry advantages into advantages for the development of the marine economy, compared to its strong foundation in land warfare and technological innovation. The marine economy faces problems such as small total volume and emerging industry scale, low degree of integration of the sea land industrial chain and uncoordinated development. Starting from the core connotation of land sea integration, the current situation and existing problems of Shenzhen’s land sea economic integration development, and the strategy integrated development of land-sea economy were deeply analyzed.

Through the study of the panel data of 17 listed city commercial banks in the five years from 2017 to 2022, the DEA-SBM-DDF model was used to measure their efficiency. The results show that the operational efficiency of urban commercial banks is generally high, but it shows a downward trend, and there are great differences in the management level among banks, and the operational efficiency of Bank of Beijing is the highest. Finally, the Tobit model was used to analyze the influencing factors affecting the operational efficiency of urban commercial banks, and the results show that the scale of Internet payment, asset scale and non-interest income non-performing loan ratio have a significant impact on the operational efficiency of urban commercial banks. The analysis results also have a certain reference role for other commercial banks’ goal setting, performance evaluation and job candidates’ selection of target commercial banks.

Under the backdrop of the Chinese government’s emphasis on the digital economy and data elements, relevant ministries and commissions further clarify the policy framework for the inclusion of data assets in corporate financial statements in 2024, thereby unlocking greater potential for data elements. Against this background, the panel data firstly used from 18 companies that had incorporated data assets into their financial statements, employing the Difference-in-Differences(DID) method to examine the impact of data asset inclusion on profitability metrics of listed companies. The findings reveal that, under the current data management framework, while the inclusion of data assets positively influences the profitability of listed companies, the impact remains relatively limited. Despite the undeniable positive significance of data asset inclusion for enterprises, the process has yet to significantly boost overall corporate profitability. This limitation arises from the fact that the value assessment system for data assets is not yet fully reflected in the existing accounting standards, and the maturity of the inclusion process requires further improvement. To better harness the potential of data assets and enable companies to reap the benefits of their inclusion, future policies should focus on optimizing the management and valuation systems for data assets, enhancing the market-driven application of these assets, and ensuring deeper integration of relevant accounting standards and regulations. These measures would help unlock the value of data assets, activate corporate data resources, and provide robust support for profit growth.