[目的/意义] 对已有的文本表示、分类算法进行组合，遴选一种复杂度低、训练时间少的组合方式，构建商品评论情感文本分类的优化模型。[方法/过程] 以Keras API为应用环境，将Word2vec词向量输入Embedding嵌入层，依据句子词索引序列，通过控制trainable参数实现3种商品评论的文本表示；将不同的文本表示分别与不同分类算法进行匹配，分析分类效果差异，确立较优算法组合。[结果/结论] Word2vec词向量输入Embedding嵌入层继续训练的文本表示方法，结合TextCNN算法训练获得的分类模型，在商品评论测试集上分类效果表现较好，准确率和ROC曲线面积AUC值分别为94.02%、0.982 7。应用表明，分类模型能较好实现商品评论的情感分类，有较好的分类泛化能力。

[Purpose/significance] The existing text representation and classification algorithms are combined, and a combination mode of low complexity and less training time is selected to construct an optimization model for the classification of emotional texts of commodity reviews. [Method/process] Firstly, this paper took the Keras API as an application environment, input Word2vec word vector into Embedding embedded layer. Then, based on sentence word index sequence, three kinds of commodity comment text representation were realized by controlling the trainable parameter. Finally，in this paper, different text representations were matched with different classification algorithms, differences in classification effects were analyzed, and the better combination of algorithms was established. [Result/conclusion] The text representation method which is continued training by Inputting Word2vec Word Vector into Embedding embedded Layer, combined with the TextCNN algorithm establishes the classification model. It performs better on the product review test set. Its accuracy and ROC curve area AUC values are 94.02% and 0.9827, respectively. The application shows that the classification model can better realize the emotional classification of commodity reviews and has better classification generalization ability.