Aiming at the multi-variable commodity sales forecasting problem, in order to improve the accuracy of prediction, an ARIMA-XGBoost-Lstm weighted combination method is proposed to predict the sales sequence of commodities with multiple influencing factors, In this study, ARIMA is used for univariate prediction. The predicted value is used as a new variable together with other variables in the XGBoost model for mining different attributes, and the predicted values of XGBoost are merged into the multivariate sequence, and then the new multidimensional data is converted. In order to supervise the learning sequence and use the LSTM model for prediction, the three model prediction results are weighted and combined, and the best combination weights are obtained through multiple experiments to calculate the final prediction value. The data results show that the multivariate prediction method based on the weighted combination of XGBoost and LSTM is more accurate than the prediction obtained by a single prediction method.