Abstract: Under the explosive growth of e-commerce orders, the ability to apply order-derived data is highly demanded. How to utilize the data for fast and dynamic prediction is the key point of online-shopping behavior study. In order to improve the stability of prediction model, this paper proposes a combination forecasting model based on integration of web search index, which incorporates BP neural network, Adaboost-based BP neural network and Support Vector Machine（SVM）. The paper also constructs an index system integrating Baidu index and order-derived information, for the sake of enhancing accuracy. Final contrast experiment results show the effectiveness of using web search indexes as an influence factor of forecasting model.

Key words: order, Baidu index, combination forecasting

摘要： 当前电商用户订单日志不断爆发式增加，日志行为数据亟需应用，在线用户订单量的快速动态预测成为研究的关键方向。为了提高订单量的预测精度，结合BP神经网络、基于Adaboost的BP神经网络和支持向量机的预测优点，提出一种基于融合网络搜索指数的组合预测模型，构建融合百度指数和电商用户订单信息的指标体系，并通过对比实验证明了网络搜索指数作为电商订单量组合预测模型影响因素的有效性。

关键词: 订单量, 百度指数, 组合预测