Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the “drone” keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI’s power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.

1. 서 론
2. 선행연구
3. 연구방법론
    3.1 분석 데이터
    3.2 BERTopic을 통한 분석방법론
4. 분석 결과
5. 결 론
Acknowledgement
References

• Jung Sun Lim(Korea Institute of Science and Technology Information) | 임정선 (한국과학기술정보연구원) Corresponding author
• Seoung Hun Bae(LXSIRI) | 배성훈 (한국국토정보공사 공간정보연구원)
• Taehoon Kwon(Korea Institute of Science and Technology Information) | 권태훈 (한국과학기술정보연구원)