Digital twin-based intelligent risk assessment and decision support system for university student entrepreneurial projects

Abstract

Abstract University student entrepreneurial ventures face significantly higher failure rates compared to traditional businesses, primarily due to inadequate risk assessment and decision-making challenges. This research develops an innovative digital twin-based intelligent risk assessment and decision support system specifically designed for student entrepreneurial projects. The system integrates digital twin technology with machine learning algorithms to create comprehensive virtual representations of entrepreneurial ventures, enabling real-time risk monitoring, predictive analytics, and intelligent decision recommendations. The proposed framework employs a multi-layered architecture encompassing data acquisition, digital twin modeling, risk assessment engines, and intelligent decision support modules. Experimental validation using 2,847 entrepreneurial projects demonstrates superior performance with 94.2% prediction accuracy, compared to 78.5% for traditional statistical methods and 85.7% for standard machine learning approaches. The system provides early warning capabilities with average lead times of 22.1 days and achieves 23.7% improvement in project success rates. Results indicate significant enhancements in decision-making effectiveness, risk mitigation capabilities, and overall entrepreneurial project outcomes, with user satisfaction scores averaging 4.47 out of 5.0. This research contributes to the theoretical understanding of digital twin applications in entrepreneurial contexts while providing practical solutions for improving student venture success rates through intelligent risk management and decision support.