Urban underground space is a complex spatial scenario that is highly susceptible to disasters. To achieve entity recognition in textual information related to urban underground space disasters, this study proposes the ALBERT-BiLSTM-CRF model. The urban underground space disaster text data is firstly encoded using the ALBERT model, which captures the deep semantic information of words in the context. The encoded data is then fed into a BiLSTM network to obtain hidden state vectors for each word, enhancing the feature representation of words. Finally, these vectors are input into a CRF layer to obtain the optimal label sequence and complete named entity recognition. The proposed model achieves an accuracy of 95.41%, a recall of 94.08%, and an F1 score of 94.74%. Comparative experiments with the BiLSTM-CRF, BERT-CRF, and BERT-BiLSTM-CRF models are conducted on the Boson dataset and our experimental dataset, demonstrating the superior performance of the ALBERT-BiLSTM-CRF model.