## 1 Introduction
These studies show that traditional competitiveness models need to be updated to adapt to the new characteristics of the digital age and the deepening development of globalization. For a long time, the assessment of national competitiveness has mainly relied on traditional frameworks such as the World Economic Forum (WEF) Global Competitiveness Index (GCI) and the International Institute for Management Development (IMD) World Competitiveness Yearbook [10; 11]. These frameworks evaluate by integrating a series of macroeconomic indicators, institutional efficiency, infrastructure, health, education,market size, labor market efficiency, financial market development, technological readiness, business maturity, and innovation capabilities. Although these traditional methods provide valuable macro perspectives, their limitations are increasingly prominent in the face of the rapidly changing global economic landscape, complex data ecosystems, and nuances in specific regions. For example, traditional indicators may not fully capture the vitality of the informal economy, the bias caused by data scarcity, and the impact of complex nonlinear relationships. In addition, their evaluation cycle is relatively long, and it is difficult to provide real-time and dynamic insights [12]. In recent years, with the rapid development of big data, artificial intelligence, and computing power, the application of algorithms in economics and development research has exploded. This provides new possibilities for overcoming the limitations of traditional economic evaluation methods. Machine learning has been widely used in economic forecasting, risk assessment, and complex pattern recognition. For example, [PERSON], [PERSON] discussed the application potential of big data and machine learning in economic research, arguing that these technologies can help economists extract valuable information from large amounts of data [13]. In terms of national competitiveness assessment, some studies have begun to try to use machine learning to construct composite indexes or prediction models. [PERSON], [PERSON], et al. used machine learning methods to predict national innovation capabilities, and the results showed that these models can provide more accurate predictions and deeper insights than traditional regression methods [14]. [PERSON], [PERSON], et al. used deep learning technology to evaluate a country's competitiveness in a specific industry by analyzing news text and social media data [15]. Network analysis has shown great potential in studying inter-country trade, technology diffusion, and knowledge flows. By constructing inter-country trade networks, technology cooperation networks, or FDI networks, we can reveal the centrality, connectivity, and vulnerability of a country in the international economic system, thereby indirectly reflecting its competitiveness. For example, the Economic Complexity Index (ECI) proposed by [PERSON], and [PERSON] measures the production knowledge and economic competitiveness of a country based on the complexity of its export product network [16]. [PERSON], [PERSON], et al. used international trade networks to analyze the resilience and vulnerability of the global economy, providing a new perspective for understanding the competitiveness of a country in the face of external shocks [17]. In addition, technologies such as natural language processing (NLP) and computer vision have also begun to be applied to economic analysis. For example, by analyzing policy texts, company reports, or social media sentiment through NLP, the effectiveness of policies or market confidence can be evaluated [18]. Computer vision technology combined with satellite image data can be used to evaluate the progress of infrastructure construction, urbanization level, or poverty situation, especially in areas with scarce data [19]. These algorithm applications provide more sophisticated, dynamic, and comprehensive data sources and analysis methods for national competitiveness evaluation, helping to make up for the shortcomings of traditional methods.