Kongyao Huang, Yongjun Zhou, Xiehua Yu, Xiaohong Su
{"title":"基于深度学习的创新创业市场趋势预测模型:案例研究与性能评估。","authors":"Kongyao Huang, Yongjun Zhou, Xiehua Yu, Xiaohong Su","doi":"10.1177/00368504241272722","DOIUrl":null,"url":null,"abstract":"<p><p>In the current economic landscape, the growing importance of innovation and entrepreneurship underscores an urgent need for accurate market trend prediction. Addressing this challenge, our study introduces an innovative entrepreneurial market trend prediction model based on deep learning principles. Through detailed case studies and performance evaluations, this paper demonstrates the model's effectiveness and its potential to enhance decision-making capabilities in a competitive business environment. Accurate market trend prediction is crucial in the fields of innovation and entrepreneurship, and our approach meets this demand. Our model leverages the power of deep learning technology, combining historical market data with diverse market indicators, including sentiment analysis derived from social media, to create an advanced predictive model that surpasses traditional methods. By analyzing data from multiple channels, our model exhibits exceptional accuracy in forecasting future market trends. The case study provides strong evidence of our model's performance and precision, showcasing its significant support for innovators and entrepreneurs navigating complex market trends. Furthermore, this study highlights the vast potential of deep learning technology in the economic sector. We emphasize the importance of developing innovative entrepreneurial market trend prediction models and foresee an increase in project success rates for innovators and entrepreneurs by enhancing decision quality through the adoption of deep learning.</p>","PeriodicalId":56061,"journal":{"name":"Science Progress","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11363233/pdf/","citationCount":"0","resultStr":"{\"title\":\"Innovative entrepreneurial market trend prediction model based on deep learning: Case study and performance evaluation.\",\"authors\":\"Kongyao Huang, Yongjun Zhou, Xiehua Yu, Xiaohong Su\",\"doi\":\"10.1177/00368504241272722\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In the current economic landscape, the growing importance of innovation and entrepreneurship underscores an urgent need for accurate market trend prediction. 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Innovative entrepreneurial market trend prediction model based on deep learning: Case study and performance evaluation.
In the current economic landscape, the growing importance of innovation and entrepreneurship underscores an urgent need for accurate market trend prediction. Addressing this challenge, our study introduces an innovative entrepreneurial market trend prediction model based on deep learning principles. Through detailed case studies and performance evaluations, this paper demonstrates the model's effectiveness and its potential to enhance decision-making capabilities in a competitive business environment. Accurate market trend prediction is crucial in the fields of innovation and entrepreneurship, and our approach meets this demand. Our model leverages the power of deep learning technology, combining historical market data with diverse market indicators, including sentiment analysis derived from social media, to create an advanced predictive model that surpasses traditional methods. By analyzing data from multiple channels, our model exhibits exceptional accuracy in forecasting future market trends. The case study provides strong evidence of our model's performance and precision, showcasing its significant support for innovators and entrepreneurs navigating complex market trends. Furthermore, this study highlights the vast potential of deep learning technology in the economic sector. We emphasize the importance of developing innovative entrepreneurial market trend prediction models and foresee an increase in project success rates for innovators and entrepreneurs by enhancing decision quality through the adoption of deep learning.
期刊介绍:
Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.