In the heart of China’s rural revitalization efforts, a groundbreaking study led by Xueli Dong from the School of Management at Zhengzhou College of Finance and Economics is harnessing the power of artificial intelligence to transform smart village construction and student entrepreneurship. Published in the journal *Discover Artificial Intelligence* (translated from Chinese as *探索人工智能*), the research combines Long Short-Term Memory (LSTM) networks and simulated annealing algorithms to tackle pressing challenges in agriculture and youth entrepreneurship.
Dong’s work addresses critical gaps in data prediction and decision support for smart rural construction. By leveraging LSTM, a type of recurrent neural network, the study accurately predicts key agricultural indicators such as crop growth cycles, pest and disease probabilities, and market price fluctuations. “The LSTM model demonstrated an impressive 92% accuracy in crop yield prediction and a 95% accuracy in price forecasting,” Dong explains. “This level of precision is unparalleled by traditional statistical methods, providing farmers with timely and actionable market intelligence.”
The implications for the agricultural sector are profound. With precise predictions, farmers can make informed decisions that enhance productivity and profitability. For instance, knowing the optimal planting and harvesting times can reduce waste and maximize yields, while accurate price forecasts enable better market strategies. This technology could revolutionize precision agriculture, making it more efficient and sustainable.
Beyond agriculture, the study also explores the optimization of entrepreneurial paths for college students. Using simulated annealing—a probabilistic technique for approximating the global optimum of a given function—the research identifies the most cost-effective and profitable entrepreneurial models. “Under the optimal path determined by the simulated annealing algorithm, the initial return on investment for entrepreneurial projects reached 35%, significantly higher than the industry average,” Dong notes. This guidance is invaluable for young entrepreneurs navigating the complexities of starting a business.
The integration of LSTM and simulated annealing algorithms offers a robust framework for decision-making in both agriculture and entrepreneurship. LSTM’s ability to process time series data makes it ideal for analyzing the dynamic changes in smart village construction, while simulated annealing efficiently finds near-optimal solutions for entrepreneurial paths. “These algorithms not only enhance the accuracy of predictions but also provide clear, actionable insights,” Dong adds.
The research published in *Discover Artificial Intelligence* highlights the transformative potential of AI in rural development and entrepreneurship. As smart village construction continues to gain momentum, the insights from this study could shape future policies and strategies. For the energy sector, the precision and efficiency gains in agriculture could lead to more sustainable practices, reducing the environmental footprint of farming activities. Similarly, the optimization of entrepreneurial paths could inspire innovative business models that drive economic growth and job creation.
In an era where technology and rural development intersect, Dong’s work stands as a beacon of innovation. By bridging the gap between advanced algorithms and practical applications, this research paves the way for a smarter, more prosperous future for rural communities and young entrepreneurs alike.