Abstract: This paper tries to effectively predict the change trend of mining pressure in the working face, thereby reducing roof accidents and guiding coal mine safety production, a mining pressure data prediction model Prophet+LSTM is proposed that integrates the influence of multiple adjacent supports. The model is first based on digital signal processing technology, the original mine pressure data is filtered through arithmetic mean filtering and wavelet denoising methods to reduce noise and random errors; secondly, based on the in-depth analysis of the characteristics of the Prophet model and the LSTM model, the method of adding additional regression variables is used to combine the rock pressure data affected by multiple adjacent supports; finally, in order to make full use of the advantages of the Prophet model and the LSTM model, a Prophet+LSTM combined model is constructed to predict the rock pressure of the working face, and the most weighted weight is obtained through the linear weighted combination method. The coefficients gradually reduce the error of the model's prediction results. The root mean square error(RMSE) and mean absolute error(MAE) are used to evaluate the prediction effect of the Prophet+LSTM model on the mine pressure time series. The results of the application of the prediction model on the time series data of the support rock pressure in a certain mine show that the RMSE and MAE values of the support rock pressure prediction results after digital signal processing have decreased by about 20% and 16%, respectively. The prediction results of the Prophet model and the LSTM model.It is better than the traditional BP neural network and ARIMA model, and the prediction method of the Prophet+LSTM model has stronger stability and higher accuracy than its single model, which can effectively predict the changes in the mine pressure during the advancing process of the working face. This prediction method provides a research idea for the prediction of underground pressure in coal mines.