E-commerce is a new business mode on a large scale and with great potential that is flourishing along with the emerging Internet technology. Forecasting short-term sales of products can help e-commerce companies respond more quickly to market changes. This study establishes a forecast model of short-term sales applied to the e-commerce accounting system based on historical data on e-commerce sales and clicks on portal products. With the adoption of AdaBoost idea, the forecast results of multiple traditional BP neural networks are assembled, leading to a higher accuracy. According to the characteristics of the short-term sales in e-commerce, we plan the timing design of time window and establish a forecast model of sales in the unit of day considering the weekend effect. Experiments show that the forecast error of this model can be controlled within 20%.