How to accurately and efficiently forecast sales data is an important issue for enterprises. Although the traditional time series prediction method is dominant in research and practice, it has some limitations. With the development of big data, e-commerce enterprises can obtain unprecedented data volume and data characteristics, and it is difficult to accurately predict sales only by using past behaviors and trends. This study proposes a risk aversion-biased combination forecasting model based on the random forest, GBDT, and XGBoost algorithm and used the cost data of each commodity to realize the accurate sample weighting and to output the forecasting results. The experimental results show that the combination forecasting model can predict sales more accurately, which is of great significance for e-commerce enterprises to reduce the cost of commodity management.