With the development of semiconductor technology, the number of transistors is growing exponentially, and voltage drop violations have become a key challenge in the electronic design and testing of very large scale integrated circuits. The dynamic voltage drop is highly dependent on the instantaneous current generated by the standard cell switching, therefore, it is of great significance to efficiently and accurately predict the dynamic currents of the standard cells. This article proposes a standard cell transient current prediction model based on LightGBM. Training data is obtained through SPICE, features are extracted, and cross validation and grid search methods are used to search for the optimal parameters of the model. The model can predict the dynamic current of a standard cell under different combinations of output toggle directions, power voltages, input transition times and output load capacitance. When using this method for modeling, there is no need for internal structural information of standard cells, and the modeling process is efficient and versatile. The experimental results show that in terms of model accuracy, the determination coefficients of the model for dynamic current prediction on various standard cells are all greater than 0.928. The model accuracy is superior to XGBoost and RFR methods; the generalization ability is superior to ANN and LSTM methods; in terms of running time, the modeling time and prediction time of this model are both shorter than those of ANN, LSTM and RFR methods. This model achieves a good balance in model accuracy and computing resources and can be used for dynamic current prediction caused by standard cell switching behavior, providing efficient and reliable support for dynamic voltage drop analysis and violation detection.