Decision-making on shield construction parameters depends on timely and accurate geological properties feedback. Real-time mastering of geological status around the shield during tunneling is necessary to achieve safe and efficient construction. However, balancing the model generalizability and the accuracy of geological identification remains a significant challenge. This paper proposes a Rapidly Geological Features Identification (RGFI) method that predicts the percentage of each stratum in the excavation face under different geological environments. The method consists of three stages: k-means-based geological type redefinition, eXtreme Gradient Boosting based geological type classification and convolutional neural network stratum distribution prediction with an attention mechanism. The method produces good generalization performance and achieves higher accuracy when simulating tunnel construction data for self-driving shield in the interval between Qian-Zhuang and Ke-Ning Road of Line 5 of Nanjing Metro in China. Furthermore, the method successfully provided more accurate geological information for the Hangzhou-Shaoxing intercity railroad tunnel project. It helped the ‘ZhiYu’ shield to adjust the construction parameters quickly and improve the safety and quality of the project.