Construction of Network Database for Innovative Entrepreneurial Practice Course

Abstract

The Innovative Entrepreneurial Practice Course offers a dynamic learning experience tailored to aspiring entrepreneurs seeking to navigate the complexities of the business world. This course provides a comprehensive overview of entrepreneurial concepts, strategies, and practical skills essential for success in today's competitive landscape. Through a combination of interactive lectures, case studies, and hands-on projects, participants gain invaluable insights into ideation, business planning, market analysis, and venture creation. The course emphasizes experiential learning, encouraging students to apply theoretical knowledge to real-world scenarios and challenges. By fostering creativity, critical thinking, and collaboration, the Innovative Entrepreneurial Practice Course equips students with the tools and mindset needed to thrive as innovative entrepreneurs in the global marketplace. This paper presents the construction of a network database tailored for the Innovative Entrepreneurial Practice Course, employing the Weighted Software-Defined Clustering Database (WSD-CD) framework. The network database serves as a comprehensive repository of resources, insights, and case studies relevant to entrepreneurial practice, offering students a rich and dynamic learning environment. Through the WSD-CD model, data is organized and clustered based on weighted attributes, allowing for efficient retrieval and analysis of information. The database encompasses a wide range of entrepreneurial topics, including business models, market trends, funding strategies, and success stories, curated from diverse sources such as industry reports, academic journals, and expert interviews. This networked approach enables students to explore interconnected concepts and gain holistic perspectives on entrepreneurial challenges and opportunities. Through simulated experiments, the efficacy of the database construction process was evaluated, yielding promising numerical results. For instance, the WSD-CD model demonstrated a 30% improvement in data retrieval speed compared to traditional clustering methods, enhancing the efficiency of information access for course participants. Additionally, the database achieved a 25% increase in user satisfaction, as evidenced by feedback surveys indicating higher levels of perceived relevance and usability.