对在线商业评论文本的情感进行挖掘，融合评论文本不同特征为分类器提供更多的信息量，提出了一种新的在线电商情感分类算法。首先，针对传统词嵌入模型无法很好地融合词语情感信息特征的不足，考虑了词嵌入特征和词性特征的多特征融合方法；其次，在两种特征融合方法的基础上采用了双通道和单通道的对比来比较分类的准确性，提出了并行的CNN和BiLSTM-Attention双通道神经网络模型；最后，使用真实的京东电商评论数据集对所提模型进行了评估，并且在实验中与不同分类算法进行对比。实验结果表明，新的混合方法具有更好的分类准确率、召回率和F1指标。“,”The emotion of online business review texts was mined, and different features of review texts were fused to provide more information for the classifier. A new online e-commerce emotion classification algorithm was proposed. Firstly, aiming at the deficiency that the the emotional information features of words cannot be fused well in the traditional word embedding model, the multi-feature fusion method of word embedding features and part-of-speech features was considered in this study. Secondly, on the basis of two feature fusion methods, the classification accuracy based on both two-channel and single-channel methods was compared. A parallel CNN and BiLSTM-attention two-channel neural network model was proposed. Finally, the real JD.COM e-commerce review data set was used to evaluate the proposed model, and compared with different classification algorithms in experiments. Experimental results show that the new hybrid method has better classification accuracy, recall rate, and F1 scores.