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In order to improve the accuracy of temperature prediction of switch gear equipment, a method for temperature prediction of switch gear equipment based on LSTM recurrent neural network is proposed. Firstly, obtain the relevant data set of the power switch gear equipment, and analyze and select the characteristic variables and data preprocessing of the original data set. Secondly, the processed data set is input to the LSTM recurrent neural network for training, and an LSTM temperature prediction model is obtained. Finally, taking the bus equipment of 6 kV switchgear as an example, and a comparison experiment of device temperature prediction is performed with various prediction algorithms. The experimental results show that: By comparing with the traditional neural network and classic recurrent neural network (RNN) temperature prediction model, the method proposed in this paper has higher accuracy for the temperature prediction of the equipment in the switchgear. It provides an effective way for active predictive maintenance. [ABSTRACT FROM AUTHOR]