Urban Rail Transit (URT) accidents not only seriously affect the safety and reliability of its operations, but also reduce service level to passengers. Based on historical URT accident data, this study develops a hybrid ensemble learning model based on a Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) for predicting accident consequences in URT. The CNN is employed to capture spatial patterns from the diverse accident data, while the BERT is applied to learn complex relations in accident text descriptions. The results of the two models are combined for classifying accident consequences. The proposed hybrid ensemble learning model was applied to predict accident consequences in Chongqing’s URT using historical accident records. It achieved a prediction accuracy of 0.805 on testing data set, which is at least 20% higher than that of commonly used machine learning models, including multilayer perceptrons, support vector machines, and Bayesian networks. Furthermore, the reapplication of the proposed model to historical accident records of the URT in Chengdu demonstrates the generalizability and reusability of the model. This study forecasts the consequences of URT accidents with high accuracy using limited historical data, which supports operators in identifying high-frequency and high-impact accidents. Consequently, targeted maintenance and timely emergency response strategies can be developed to decrease accident rates and mitigate the impacts.