Abstract: Based on the long short-term memory neural network model with Attention mechanism (Attention-LSTM), a combined variable weight model consisting of Attention-LSTM neural network and Extreme Gradient Boosting (XGBoost) is used to analyze and predict the effect of electrode movement speed on the discharge parameters during electrostatic discharge. The combined model takes full advantage of the time-series characteristics of electrostatic discharge parameters and uses the Attention mechanism to highlight the input features that play a key role in the prediction of discharge parameters. Based on the original experimental data provided by the experimental results of the newly developed electrode velocity effect tester, the analytical data were preprocessed using the split-box method to obtain brand-new experimental data. Then the obtained new experimental dataset is used as the input data for the two models, and the Attention-LSTM model and XGBoost model are trained separately, and the prediction results and errors of the respective models are solved respectively. Afterwards, by using the error inverse method, we recalculated the proportional weights of the prediction results of the two models and solved the final prediction results according to the calculated weights. The control group models in the paper are: Attention-LSTM neural network model, XGBoost model, and the fixed-weight combination model. The prediction results show that compared with the basic Attention-LSTM neural network model, XGBoost model, and the fixed-weight combination model, the variable-weight combination model constructed in this paper improves the coefficient of determination in the evaluation index by 5.22%, 9.11%, and 3.13%. The combined model proposed in this paper outperforms other models in terms of prediction accuracy and algorithmic robustness, and is useful for exploring the trends and patterns of small gap electrostatic discharge parameters.