A TBLC-rAttention deep neural network model was designed for the problem of Chinese medical text sentiment recognition using deep learning technology. This model makes full use of the advantages of Bi-LSTM, Attention mechanism, and CNN. First, the Bi-LSTM is used to extract the context semantic features of the text, and the rAttention mechanism is introduced in the process; second, the CNN is used to extract the local semantic features of the text, and the text information is further mined to obtain the final semantic feature vector. Third, when calculating weighted global semantic features, r attention schemes replace traditional single attention schemes. Different attention schemes can learn sentence representations with different focuses, which can extract more valuable feature information. Finally, use the obtained semantic feature vectors to complete the text emotion recognition task of TCM reviews. The model is implemented and trained with Google ’s TensorFlow framework. The experiments show that the model has good convergence speed and accuracy, can maximize the semantic feature information, and effectively solve the problems of high dimensionality, high sparseness, The feature expression ability is weak, and it is not suitable for problems such as large data sets.