The traditional power system frequency prediction model is difficult to meet the requirements of high precision and high speed for the operation optimization and digital transformation of the power system

especially when interfacing with a large language model. Therefore

this paper proposes a frequency prediction strategy based on the LSTM-EEFM framework

aiming to improve the intelligence of power grid operation. Firstly

through qualitative and quantitative analysis

the key features affecting frequency prediction are identified

and the advantages of the LSTM model in dynamic data feature capture and ensemble learning in static data processing are combined to improve the prediction accuracy while balancing the time efficiency and meeting the multi-functional requirements of the large language model interface. Secondly

the simulation study is based on the frequency response data of the low-frequency load shedding scheme

which is more suitable for engineering practice

and the effectiveness of the proposed strategy is verified. The simulation results show that the LSTM-EEFM framework can adapt well to the varying accuracy and speed requirements of the large language model interface and has good application prospects in frequency prediction for the smart grid.