International Journal of Big Data Intelligent Technology, 2025, 6(2); doi: 10.38007/IJBDIT.2025.060213.
Chuqing Zhang
Business School, Beijing language and culture university, 100083, Beijing, China
With the increasing number and complexity of research projects, traditional manual review methods have significant limitations in terms of efficiency and consistency. To improve the scientific rigor and intelligence of project review, this paper introduces deep learning algorithms and multi-source data processing technology to construct an intelligent auxiliary decision-making system for project review. The system performs semantic encoding and indicator mapping on the application materials, attachments, and historical scoring data to achieve multi-indicator collaborative reasoning and comprehensive score generation, and dynamically corrects the results by incorporating implicit expert scoring logic. Experimental results show that the system demonstrates good discriminative ability in project semantic mapping and indicator scoring. Taking project P3 as an example, its innovation score is 0.90, and its feasibility score is 0.85. The system exhibits good performance in project semantic understanding, scoring accuracy, and ranking consistency, providing a scalable, efficient, and interpretable intelligent auxiliary tool for research project review.
Intelligent Assisted Decision-Making System for Project Review; Deep Learning Algorithm; Multi-Source Data Processing; Semantic Mapping; Comprehensive Scoring and Ranking
Chuqing Zhang. Intelligent Assisted Decision-Making System for Project Review Integrating Deep Learning Algorithms. International Journal of Big Data Intelligent Technology (2025), Vol. 6, Issue 2: 123-129. https://doi.org/10.38007/IJBDIT.2025.060213.
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