Relevant information of railway accidents, existing in the form of accident overview texts, is of great significance to railway safety work. However, due to the lack of effective information extraction methods, the knowledge of railway accidents scattered in the texts has not been fully utilized. Named entity recognition is an important subtask of information extraction, and there are few studies on named entity recognition of accidents. A named entity recognition model fused with character position features is proposed for the named entity recognition of railway accidents. The model obtains the character position features through a fully connected neural network. It merges them with the character vectors at the semantic level as the final vector representation of the characters, which is then input to the BiLSTM-CRF model to obtain the optimal label sequence. The experimental results show that the accuracy, recall, and F1 value of the model on the named entity recognition of railway accident texts are 93.29%, 94.77%, and 94.02% respectively. This model yields better effects than traditional models and lays a foundation for the construction of a railway accident knowledge graph.