To effectively improve the maintenance efficiency and fault location accuracy of high-speed railway turnouts, a combined model for multi-level fault diagnosis of high-speed rail turnouts based on character and word fusion was proposed. Firstly, a professional thesaurus of high-speed rail turnout equipment was established, and fault texts were represented as character vectors and word vectors and the character vectors and word vectors were deeply fused. Secondly, considering the problem of imbalanced categories in fault texts, the Borderline-SMOTE algorithm was used to process the imbalanced text data to optimize the fault text data distribution. Then, a combination of Bi-directional long short-term memory ( BiLSTM) and convolutional neural network ( CNN) was used to extract deep features of the fault text. Finally, an intelligent diagnosis of faults was achieved by means of a classifier. The model performance was validated using fault text data of China high-speed railway turnout faults. The test results show that the accuracy of the proposed model reaches 95. 62% for the primary fault diagnosis and 93. 81% for the secondary fault diagnosis, which proves that the multi-level fault diagnosis accuracy can reach the desired effect.