共享经济的出现为消费者多元化、个性化需求升级提供了新的商业模式，相应地，相关参与企业的关注点逐步由传统的供给侧视角（“投入—产出”）转向需求侧视角（“产品特征—需求”）。与此同时，网络经济的发展累积了海量数据，为大数据辅助决策及人工智能的应用提供了可能。基于此，将消费者需求特征与大数据环境纳入建模考量成为评价理论研究领域的前沿与热点。本项目主要围绕面向大数据的需求侧DEA理论这一主题展开。首先构建需求侧DEA模型，通过新模型对共享经济的需求特征展开相关分析；进而，探究大数据环境下新模型的高效计算方法，包括前沿面学习理论以及模型的分解与合并算法；最后，通过在重庆市共享短租市场的应用研究，验证所提理论与方法的合理性与适用性，为共享经济实践活动中的需求预测、产品定价、精准营销等决策提供相应的支持与参考。

The emerging of sharing economy had provided new business mode for meeting the various and personalized consumer demand. Correspondingly, more and more corporations have shift from production orientation to demand orientation mode. Meanwhile, with the development of web economy, piles of data have accumulated to provide the possibilities of big-data decision-making and artificial intelligence. Therefore, the model construction that combined both demand analysis and big data environment had become the hottest and frontier issue in evaluation research field. This research mainly focuses on the big data oriented demand-side DEA theory. Firstly, we construct demand-side DEA model to analyze demand characteristics of sharing economy. Secondly, we investigate the efficiency calculation methods of the new proposed models, including the learning theory of frontier and the decomposition and merging of big model. Lastly, we verify the scientifically and practical applicability of this theory using short-rent data of Chongqing city. In general, this research would provide correspondingly academic support for demand forecasting, product pricing and targeted marketing in practical sharing economy.