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.

Considering the process of digital technology adoption by construction enterprises, where imitation learning and herd effect were factored in, game theory was employed to construct both a decision model and a diffusion model for enterprise groups concerning digital technology issues. Through numerical simulation, the impact of exchange benefits, exchange intensity, and initial adoption ratio of construction enterprises on their digital technology behavior was studied, and further strategies to promote the adoption and diffusion behavior of digital technology in enterprises were explored. The result shows that the higher the exchange benefits, the more companies tend to adopt digital technology. The exchange intensity has a positive driving effect on accelerating the adoption and diffusion of digital technology. The higher the initial adoption ratio, the more effective it is in promoting the widespread formation of digital technology adoption decisions.

As the difficulty of financing for manufacturing enterprises in the capital market becomes increasingly prominent, improving the quality of information disclosure and enhancing institutional investors’ confidence have become crucial issues in corporate financing strategies. Based on data from Chinese manufacturing listed companies from 2013 to 2022, the impact of the quality of Management Discussion and Analysis(MD&A) on institutional investment intentions and the mediating role of internal control in this relationship were examined. The results indicate that there is a significant positive correlation between the quality of MD&A and institutional investment intentions, with internal control quality playing a partial mediating role. High-quality MD&A improves the transparency and credibility of a company’s information disclosure, thereby enhancing institutional investors’ confidence. Effective internal control further ensures the accuracy and completeness of the information disclosed, boosting institutional investors’ confidence and willingness to invest. Additionally, non-state-owned enterprises disclose higher-quality MD&A than state-owned enterprises, with institutional investors particularly focusing on transparency and information quality. This research provides important theoretical foundations and practical guidance for manufacturing enterprises to improve financing efficiency and strengthen the management of non-financial information disclosure.

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.

In the future, the characteristics of technology transformation and industrialization in the industrial field are long, and the risks are large, which puts forward new requirements for incubators and other business incubation carriers. Using field investigation, case analysis and other research methods, this paper sorted out the future technological innovation, future industrial growth mechanism and demand for incubation services, analyzed the current situation of incubation carrier construction and development, and the challenges faced by the supply of technological innovation and entrepreneurship services in the future. The research shows that the incubation vector is relatively insufficient in quality project screening and mining, innovation resource matching, professional service supply and other aspects. It is suggested to adjust and optimize from the aspects of promoting market-oriented operation, strengthening the recruitment of quality projects, and building professional service platforms.

In the context of rising trade protectionism and intensifying geopolitical competition, both endogenous drivers and exogenous pressures for the outward transfer of China’s manufacturing sector have increased. Based on the estimation of the scale of China’s manufacturing outward transfer using the ADB input-output table, text analysis and empirical analysis methods were employed to examine the “endogenous drivers” and “exogenous pressures” of China’s manufacturing outward transfer. The results indicate that both endogenous drivers and exogenous pressures coexist in the outward transfer of China’s manufacturing sector, with endogenous drivers being the primary force, but exogenous pressures are gradually becoming an important driving factor. The overall endogenous drivers for the outward transfer of China’s manufacturing include market seeking, technology acquisition, resource acquisition, utilizing cheap labor costs and host country institutional environments. Meanwhile, the exogenous pressures include manufacturing reshoring and trade frictions. However, when considering the motivations of utilizing cheap labor costs and resource acquisition, China also takes into account labor quality and the host country’s infrastructure conditions. Heterogeneity analysis reveals that the transfer to developing countries is mainly driven by endogenous factors, while the transfer to developed countries, as well as the outward transfer of low-tech and medium-high-tech manufacturing, is influenced by both endogenous drivers and exogenous pressures. Additionally, manufacturing reshoring has a greater driving effect on the outward transfer of China’s medium-high-tech manufacturing.

New-generation information technologies, such as big data and cloud computing, are leading industrial transformation and propelling the global shift towards the digital economy era. Digital transformation has emerged as a new competitive frontier in the global arena and a new engine for China’s economic development. By integrating traditional production factors, optimizing resource allocation, enhancing automation and intelligence, and reducing production costs, digital transformation is fostering efficiency gains. The real estate industry faces complex challenges, prompting real estate enterprises(REEs) to compete in leveraging new technologies for digital transformation to cut costs, enhance efficiency, and facilitate diverse industrial layouts. A shared service system based on a digital middle platform architecture was widely applied in diversified businesses, aiding REEs in nurturing data assets, achieving full-link system integration, and responding to business innovation needs, thereby reshaping their core competitiveness. By systematically reviewing theories on digital transformation and digital middle platform architectures, and conducting in-depth research on REEs to analyze the process and application logic of digital transformation based on a middle platform architecture, business scenarios before and after transformation were compared to reveal the challenges and value creation in the digital transformation of REEs, providing recommendations for their digital transformation journey. The research indicates that the digital transformation of REEs involves establishing a digital foundation through three stages of informatization, online enablement and intelligence, gradually building a digital middle platform, adjusting organizational structures, integrating ecological resources and enabling intelligent decision-making. This transformation holds profound significance for enhancing their digital leadership, business innovation capabilities, and promoting high-quality and sustainable enterprise development.

By constructing a comprehensive evaluation system for the digital transformation of manufacturing and the development of the digital economy, the coupling and coordination status of these two systems across 30 provinces in China from 2011 to 2019 were analyzed. Their dynamic changes and regional disparities from multiple dimensions were further examined. The research reveals that during this period, both the level of digitalization in manufacturing and the development of the digital economy show an upward trend across all provinces. However, there remains a significant gap between the achieved level of system coupling and the benchmark for high coupling. Despite this, the overall coupling and coordination degree has been on an upward trend, indicating a positive interaction and progress between the two domains. Regional analysis demonstrates that the coupling degree among provinces first increases and then decreases from east to central and western regions. Conversely, the coupling and coordination degree decreases progressively from the eastern to the western region.

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.

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.