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With the rapid development of electronic products and integrated circuits, the fault prediction research on electronics has attracted great attention. However, it is still very difficult to accurately predict electronics’ service life. At present, the main implementation is state monitoring and health management. A state prediction model for integrated RF module temperature based on long short-tern memory network（LSTM）neural network is constructed in this paper, Firstly, the time-domain data of a device is converted into a supervised sample dataset. Then the original parameter set, and training and test sets for prediction model are established. Next the LSTM deep learning network is constructed and run with parameters. Finally, the error curves of the predicted and observed values are obtained. The model is proved to achieve a good prediction effect and forecast precision with an accuracy of 98.7% when predicting the integrated RF module temperature in a typical task.