Based on the comparison of several basic models, a prediction model based on LightGBM and support vector regression model is proposed in this paper. This model not only extracts the features of the user's behavior data and the features of commodity attributes, but also combined with the advantages of time sliding window in feature processing, extracts dynamic features by using the sale data of the commodity and correlation data, and then uses the fusion of multiple models to predict the

more »

... y data. The experimental results show that after the feature extraction of sliding window method, by comparing support vector regression model and LightGBM prediction model, it is found that the effect of LightGBM prediction model is slightly better than the support vector regression model. By combining the support vector regression model and the LightGBM model, the root-mean-square error of the supermarket sales forecast model is 1.23209, which is significantly higher than the single model prediction results. Therefore, this model is an effective method to predict the sales volume of short-term supermarket.