In order to study fracture mechanism of rocks in di?erent brittle mineral contents, this study proposes a method to identify the acoustic emission signal released by rock fracture under di?erent brittle mineral content (BMC), and then determine the content of brittle matter in rock. To understand related interference such as the noises in the acoustic emission signals released by the rock mass rupture, a 1DCNN-BLSTM network model with SE module is constructed in this study. The signal data is processed through the 1DCNN and BLSTM networks to fully extract the time-series correlation features of the signals, the non-correlated features of the local space and the weak periodicity law. Furthermore, the processed signals data is input into the fully connected layers. Finally, softmax function is used to accurately identify the acoustic emission signals released by different rocks, and then determine the content of brittle minerals contained in rocks. Through experimental comparison and analysis, 1DCNN-BLSTM model embedded with SE module has good anti-noise performance, and the recognition accuracy can reach more than 90 percent, which is better than the traditional deep network models and provides a new way of thinking for rock acoustic emission research.