## 1 Introduction
In the era of big data and artificial intelligence, the application of algorithm technology in the fields of economics, sociology, and policy analysis has become increasingly in-depth, bringing revolutionary opportunities for national competitiveness evaluation. Advanced algorithms such as machine learning, deep learning, network analysis, and natural language processing can process massive, multi-source, and heterogeneous data; mine complex patterns and nonlinear relationships that are difficult to discover with traditional statistical methods; and build more predictive and adaptive evaluation models [20]. Regarding national competitiveness, relatively few studies have focused on the African continent, and they often face unique data and methodological challenges. Most studies focus on the overall growth of the African economy, trade patterns, foreign direct investment (FDI) attractiveness, governance levels, and regional integration processes. For example, [PERSON] conducted an in-depth analysis of the drivers of economic growth in Africa, emphasizing the importance of institutional quality and human capital in improving the competitiveness of African countries [21]. [PERSON] explored the role of information and communication technology (ICT) in promoting inclusive development and competitiveness in African countries, pointing out that digital inclusion is crucial to narrowing the development gap [22]. However, competitiveness research on French-speaking African countries is even scarcer and is often limited to macroeconomic analysis or case studies of specific sectors. For example, some studies have focused on the impact of trade integration within the West African Economic and Monetary Union (UEMOA) on the economic performance of its member countries [23]. Other studies may focus on the effect of improved business environments in specific African countries on their attraction of FDI [24]. These studies provide important regional and country insights, but they generally face the following challenges: (1) data availability and quality issues: many French-speaking African countries have incomplete or untimely official statistics that make it difficult to support detailed analysis [25]; (2) traditional indicators fail to fully capture the huge size of the informal economy and its complex impact on competitiveness; (3) the diversity of countries in the region makes the application of a common framework challenging. Based on the above literature review, first, the global national competitiveness research has formed a relatively mature theoretical framework and evaluation system (such as WEF and IMD), but these traditional methods have limitations in data sources, timeliness, and nonlinear relationship capture; especially when facing the complexity of specific regions such as Africa, their applicability may be limited. Second, although the competitiveness research on African countries has increased, it generally faces challenges in data quality and availability and lacks a systematic and in-depth competitiveness evaluation of the specific group of French-speaking African countries. Existing research mostly stays in the analysis of macroeconomic indicators and fails to make full use of emerging data and algorithmic technologies to reveal its unique competitiveness drivers and development potential. Finally, the application of algorithmic technology in the field of economics and development research has shown great potential, from predictive models to network analysis to the use of non-traditional data sources; it has brought revolutionary changes to economic evaluation. However, it is still a significant research gap to systematically and specifically apply these advanced algorithmic technologies to the national competitiveness evaluation of French-speaking African countries and build a comprehensive, data-driven evaluation framework.