The dynamic of soil moisture in the permafrost region of the Qinghai-Tibet Plateau has a significant impact on the regional hydrological process and the stability of the ecosystem. However, due to the complex climatic and topographic conditions in the permafrost region, traditional soil moisture monitoring and prediction methods are time-consuming, labor-intensive and difficult to implement. Therefore, using deep learning methods to simulate soil moisture content in the permafrost region of the plateau has become an effective approach. Based on the long short-term memory (LSTM) model, the LSTM model was constructed and trained by introducing multi-source data, including key variables such as soil temperature, radiation, air temperature, precipitation, and relative humidity, soil moisture in the permafrost region of the Qinghai-Tibet Plateau was simulate. The results show that the LSTM model can effectively capture the dynamic changes of soil moisture in the permafrost region and simulate the variation characteristics of soil moisture content over time under different meteorological conditions. The root mean square error (RMSE) of the model in Tanggula, Tianshuihai and Xidatang areas are 0.018, 0.023 and 0.033 respectively, and the coefficient of determination (R²) are 0.937, 0.873 and 0.986 respectively. It is concluded that the LSTM model has significant advantages in processing nonlinear and time series data, especially in the complex permafrost environment, it shows lower errors and more stable numerical performance. The result provides a new idea for soil moisture simulation based on deep learning and offers a reference for revealing the variation law of soil moisture content under freeze-thaw conditions.