A study on prediction for daily production per well is of great significance in oilfield production. Under complex production conditions of oil wells, it is difficult to accurately predict daily production. A production model based on multi-variable time series data is built in this paper. Based on the convolutional neural networks - gate recurrent unit (CNN-GRU), deep features are extracted for timing prediction, and a decision tree model (LightGBM) based on the gradient boosting framework provides prediction results from the perspective of regression prediction. The results of the two are integrated with each other to further improve the accuracy of production prediction. A method that supports multi-variable time series prediction or regression prediction to accurately predict production under unknown input characteristics – strategy for recursive prediction of advance parameters is proposed. This method is used to predict important features that affect production in advance, and the predicted important features are used in simulation tests on production prediction. The simulation results show that the model established in this paper works best with the advance parameter recursive strategy and has the maximum prediction accuracy on the test set. Compared with univariate time series prediction and regression prediction models, the prediction accuracy can be significantly improved.