In the modern economy, venture companies serve as key drivers of national competitiveness by fostering innovation, creating employment, and accelerating technological advancement across diverse industries. Despite their critical role, previous venture certification systems have shown limited effects on improving profitability or resolving technical inefficiencies, primarily due to their reliance on static evaluation criteria and one-size-fits-all support frameworks that fail to account for the heterogeneous characteristics of individual firms. To address these limitations, this study proposes an automatic diagnosis and customized growth guide system that integrates multi-source data integration technology, dynamic weight allocation, and multi-objective optimization algorithms. The proposed system collects and harmonizes data from multiple sources, including financial statements, patent databases, and market trend indicators, to construct a comprehensive diagnostic profile for each venture company. A dynamic weight allocation mechanism adjusts evaluation criteria in real time based on industry-specific conditions and firm-level growth stages, thereby enhancing diagnostic accuracy and relevance. Simulation results using 10,000 virtual datasets demonstrated that the OCR-combined data pipeline achieved a 98.5% missing value completion rate, while the dynamic weight model attained 92.4% prediction accuracy in classifying firm growth potential. Furthermore, the optimal action plan recommendation utilizing the Knapsack algorithm reduced the required budget by 34.2% and time by 38.1%, significantly alleviating technical inefficiencies. These findings suggest that the proposed system can serve as a practical and scalable policy tool for enhancing the effectiveness of venture support programs, ultimately contributing to sustainable growth and improved resource allocation in the national innovation ecosystem.

1. 서 론
    1.1 연구의 배경 및 필요성
    1.2 연구의 목적 및 연구 질문
2. 이론적 배경 및 선행 연구 고찰
    2.1 벤처기업 인증 제도의 변천과 정책적 파급효과
    2.2 패널 데이터를 이용한 재무 성과 개선 연구
    2.3 벤처 인증이 기업 성과에 미치는 영향: 재무적관점(DID 모형 분석)
    2.4 벤처인증이 기술적 비효율성에 미치는영향(SFA)
    2.5 혁신 역량과 산업재산권의 매개 효과:구조방정식 모형(SEM)
    2.6 선행 연구의 한계점 및 제안 시스템의 이론적당위성 도출
3. 다중 소스 연동 기반 자동 진단 시스템 아키텍처 설계
    3.1 시스템 요구사항 분석 및 클라우드 3-Tier망분리 아키텍처
    3.2 하이브리드 데이터 수집 모델 및 OCR페일오버
    3.3 다차원 레이더 차트 매핑 및 능동형시뮬레이션
4. 산업별 동적 가중치 할당 및 최적 성장 경로 추천 알고리즘
    4.1 산업군 특성 반영 동적 가중치 스위칭 엔진
    4.2 Zero-One 배낭 문제(Knapsack Problem)기반 최적 보완 수단 탐색
5. 실험 및 시스템 성능 평가
    5.1 실험 환경 및 시뮬레이션 데이터셋(K-Startup) 생성
    5.2 [실험 1] 하이브리드 데이터 수집 모듈(OCR)성능 분석
    5.3 [실험 2] 동적 진단 엔진의 합격 예측 정확도검증
    5.4 [실험 3] 최적 보완 시나리오 추천 알고리즘의경제적 효용성
    5.5 [실험 4] 기술적 비효율성 개선 시뮬레이션
6. 결론 및 향후 과제
    6.1 연구 결과 요약
    6.2 이론적 기여
    6.3 실무적 시사점실무적 관점에서
    6.4 연구의 한계점 및 향후 연구 방향
References

• JeongSup Kum(Korea Rating & Data Co., Ltd.) | 금정섭 (한국평가데이터(KoDATA) 전문위원)
• Hee Joo(The Pool Inc.) | 주희 (더풀 대표이사)
• Hyoungtae Kim(Department of AI Management, Woosong University) | 김형태 (우송대학교 AI경영학과) Corresponding author