Abstract: In order to extract consumers'emotion information about cigarette products from massive evaluation data, word frequency, pointwise mutual information, left and right information entropy were used to extract specialized vocabulary in the field of tobacco, and the accuracy of text segmentation was promoted through establishing a text segmentation supplementary dictionary. An emotion classification model, BiLSTM-Att, for cigarette consumers'evaluation was developed by combining Bi-directional Long Short-Term Memory(BiLSTM)with Attention Mechanism. Based on the consumer evaluation data for 2 066 cigarette brands from 2006 to 2021, the developed BiLSTM-Att model was verified and compared with six opted classification methods. The results showed that: the F1 value of the BiLSTM-Att model increased by 1.78 percentage points after unifying product names; the BiLSTM-Att model featured higher accuracy in the binary and ternary emotion classification with F1 values of 92.89% and 80.12% respectively. This method provides a support for cigarette product design, precision marketing and brand development.