This paper mainly presents the research of named entity recognition of medical texts generated by online inquiry. Using the data of online medical quiz website, we employ {B, I, O} annotation system to build data sets, and extract four medical entities of disease, treatment, examination, and symptom. Taking BiLSTM-CRF as the benchmark model, two deep learning models IndRNN-CRF and IDCNN-BiLSTM-CRF are proposed, and the validity of the model on the self built dataset is verified. The two new models are compared with the benchmark model by experiment. It is concluded that the model IDCNN-BiLSTM-CRF has an F1 value of 0.8165, which exceeds the BiLSTM-CRF's F1 value of 0.8009. The overall performance of IDCNN-BiLSTM-CRF is better than that of BiLSTM-CRF. The IndRNN-CRF model has a high precision rate of 0.8427, but its recall rate is lower than the benchmark model BiLSTM-CRF.