Aiming at the inefficiency of gas turbine combustion chamber fault warning due to the scarcity of sample data, a method based on transfer learning and convolutional neural networkbidirectional gated recurrent network (CNN-BiGRU) is proposed. Firstly, the CNN-BiGRU network is pre-trained in the source domain using K-fold cross validation and the optimal model is filtered out; then the target domain fault warning model is obtained using transfer learning; then the target domain data is imported into the model and a sliding window is used to reduce the false alarms caused by the abnormal data points; finally, the warning thresholds computed using the triple standard deviation (3-sigma) method are used to determine whether a fault occurs. The experimental results show that compared with the non-transfer learning model, the proposed method reduces the root mean square error by 87.5%, the mean absolute error by 89.05%, and improves the r-squared by 6.39% when there is insufficient sample data; and compared with the system alarm moment, it is able to detect the signs of faults earlier, and warn the gas turbine combustion chamber of faults 82 min in advance.