Crowdfunding has emerged as a widespread strategy for startups seeking financing, particularly through reward-based methods. However, understanding its economic impact at both micro and macro levels requires thorough analysis, often involving advanced studies on past campaigns to extract insights that aiding companies in optimizing their crowdfunding project types and launch methodologies. Such analyses are often beyond the scope of basic data analysis techniques and frequently demand advanced machine learning tools, such as distributed computing, due to the large volume of data involved. This study aims to investigate and analyse the targets of reward-based crowdfunding campaigns through machine learning techniques, employing distributed models and structures. By harnessing the power of distributed computing, it unravels intricate patterns and trends within crowdfunding data, thereby empowering companies to refine their strategies and enhance the efficacy of their funding endeavors. Through this multifaceted approach, a deeper understanding of the economic dynamics underlying crowdfunding ecosystems can be attained, fostering informed decision-making and sustainable growth within the startup landscape.

翻译：摘要：众筹已成为初创企业寻求融资的广泛策略，尤以奖励式众筹为典型。然而，理解其在微观与宏观层面的经济影响需要深入分析，通常涉及对过往众筹项目的综合研究，以提取有助于企业优化众筹项目类型及启动方式的见解。此类分析往往超出基础数据分析技术的范畴，并常因涉及海量数据而需借助分布式计算等高级机器学习工具。本研究旨在通过机器学习技术，采用分布式模型与结构，探究并分析奖励式众筹项目目标。借助分布式计算能力，揭示众筹数据中的复杂模式与趋势，从而赋能企业优化其策略、提升资金筹措效能。通过这种多维度方法，可更深入理解众筹生态系统的经济动态，助力初创领域做出明智决策并实现可持续增长。