Attention-based Bi-directional Long Short-Term Memory neural network and Conditional Random Field (AttBi-LSTM-CRF) model was proposed for the corpus core entity recognition and core entity sentiment analysis task of Sohu coreEntityEmotion_train. Firstly, the text was pre-trained, each word was mapped into a low-dimensional vector with the same dimension. Then, these vectors were input into the Attention-based Bi-directional Long Short-Term Memory neural network (AttBi-LSTM) to obtain the long-term context information and focus on the information highly related to the output label. Finally, the optimal label of the entire sequence was obtained through the Conditional Random Field ( CRF) layer. The comparison experiments were conducted among AttBi-LSTM-CRF model, Bi-directional Long Short-Term Memory neural network (Bi-LSTM), AttBi-LSTM and Bi-directional Long Short-Term Memory neural network and Conditional Random Field (Bi-LSTM-CRF) model. The experimental results show that, the accuracy of AttBi-LSTM-CRF model is 0.78, the recall is 0.667, and the F1 value is 0.553, which are better than those of the comparison models. The superiority of AttBi-LSTM-CRF performance is verified.