期刊
RESOURCES POLICY
卷 80, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.resourpol.2022.103212
关键词
Deep learning; AI Algorithms; Entrepreneurship resources; Sustainable development
Under the rapid economic development trend, exploring resource optimization strategy of cultural and creative enterprises is crucial for sustainable socio-economic development. This study uses a recommendation system and neural network algorithm to model project features, user behavior, and content features in the cultural and creative industry. The constructed model significantly improves recognition accuracy and reduces prediction errors, providing experimental references for subsequent sustainable development and entrepreneurial resource optimization.
Under the rapid economic development trend, exploring the resource optimization strategy of cultural and creative enterprises for sustainable socio-economic development is highly relevant. This study applies the recommendation system to decision-making and resource optimization of entrepreneurial projects for the current situation of complexs entrepreneurial enterprises in the cultural and creative industry (CCI). The neural network algorithm (NNA) is adopted to model project features, user's behavioral and content features. Finally, a recommendation and resource optimization model based on NNA is constructed for CCI-related entrepreneurial projects, and the model is evaluated and analyzed. The results demonstrate that with the increase in the training period, the model's recognition accuracy reaches 81.64%. Besides, the prediction error of the recommender system is minimized when the word vector length is 200, and the number of implied features is 200. Therefore, the entrepreneurial project recommendation and resource optimization model can significantly improve the recognition accuracy and reduce prediction errors, providing experimental references and contributing to the subsequent sustainable development of social economy and entrepreneurial resource optimization.
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