Abstract: With the development of the Internet, content marketing has gradually become the mainstream of e-commerce marketing, and the daily gross merchandise volume（GMV） is directly related to the inventory optimization control and advertising strategy of enterprises. In order to improve the prediction accuracy, based on the real e-commerce order data set, according to the content marketing index, the influence of user behavior on GMV is analyzed, and a combination model of long short-term memory network（LSTM） and regularized extreme learning machine（RELM） is proposed. The experimental results show more accurate prediction and faster running speed of the LSTM-RELM model proposed in this paper, compared with the traditional machine learning models and combination models, such as LSTM-LSTM, LSTM-SVR, GM（1, 1）-BPNN. The model can provide reference for advertising strategy and inventory optimization suggestions for relevant sales enterprises.

Key words: long short-term memory network（LSTM）, extreme learning machine（ELM）, GMV prediction, combination forecasting

摘要： 随着互联网的发展，内容营销逐渐成为电商营销的主流，而该类营销的日商品交易总额（gross merchandise volume，GMV）直接关系到企业的库存优化控制与广告投放策略。为了提高预测精度，基于真实电商订单数据集，根据内容营销的指标，分析用户行为对于GMV的影响，提出了一种长短期记忆网络（long short-term memory network，LSTM）与正则化极限学习机（regularized extreme learning machine，RELM）的组合模型LSTM-RELM。实验结果表明，相比于传统单一模型与双LSTM、LSTM-SVR、GM（1，1）-BP等组合模型，LSTM-RELM模型具有更精确的预测效果与更快的运行速度，能为相关销售企业提供广告投放策略参考与库存优化建议。

关键词: 长短期记忆网络（LSTM）, 极限学习机（ELM）, GMV预测, 组合预测