To determine the weight of the combined forecasting model is very important to improve the accuracy of the model. Applying the regularization and cross-validation to the combined forecasting model based on the least squares method is for studying whether the regularization and cross-validation can improve the prediction effect of the combined forecasting model. It is carried out by adding the L₁ and L₂ norm regularization terms to the optimization solution of the combined model and using leave-one-out-cross-validation in the data set. The result shows that both the L₁ and L₂ norm regularization can improve prediction accuracy of the combined model to a certain degree. Moreover, the L₁ norm regularization is better than the L₂ norm regularization for the combined forecasting model, and the more single forecasting models participating in the combined forecasting, the better the regularization improvement effect. In addition, there is a positive correlation between the cross-validation improvement effect and amount of experimental data given.