Abstract: Realizing the intelligent warning of rock burst in coal mine is of great significance to ensure the safety of mine operation. Based on the intelligent classification prediction of rock burst occurrence time series in roadway of steeply inclined ultra-thick coal seam in a mine in Xinjiang, the spatio-temporal evolution of each microseismic information index during roadway excavation was analyzed. The Random Forest optimized by Genetic Algorithm (GA) was used. RF selects a number of indicators with high performance in predicting the development trend of impact. Based on the Phase Space Reconstruction (PSR) technology, the data is mapped to the high-dimensional space for reconstruction. LSTM is trained to learn the characteristics of high dimensional data, and a prediction model of steeply inclined ultra-thick coal seam rock burst (PSR-LSTM) based on deep learning and multiple chaotic time series is constructed. The results show that each microseismic information index is sensitive to shock warning and has significant correlation with each other. Six microseismic information indexes with high performance in predicting the development trend of shock are selected. The time series of multiple indicators has chaotic characteristics. After phase space reconstruction, LSTM learning and training can effectively enhance the data utilization rate and prediction accuracy of the model. When the prediction time of the constructed PSR-LSTM model is specified as 1 day, the prediction accuracy can reach 0.9135, and the F₁ value can reach 0.9116. All of them are better than the unreconstructed LSTM model. The model can well predict the time series trend and danger level of the rock burst in the excavation roadway of steeply inclined ultra-thick coal seam. The research method can provide reference for the intelligent prediction and early warning of rock burst in the excavation roadway of steeply inclined ultra-thick coal seam.