Aiming at the problems of limited annotation data, scarce corpus and slow progress of Khmer sentence-level sentiment analysis, this paper proposed a Khmer sentence-level sentiment classification method based on deep semi-supervised CNN model. This method combined the separate convolution for word and lexicon embeddings, used a small amount of existing Khmer sentiment lexicon resources to improve sentence-level sentiment classification task performance. First, it constructed the word and lexicon embeddings of Khmer sentence, used different convolution kernels to convolve two-part embeddings respectively, integrated the existing sentiment lexicon information into the CNN model. After the max-over-time pooling, obtaining the maximum output feature. The maximum output features of the two parts were stitched together as the input of the full connection layer. And then, it used the semi-supervised learning method of temporal ensembling training the deep neural network, reduced the need for annotated corpus, and further improved the accuracy of the model’s sentiment classification. The result proves that through the semisupervised method of temporal ensembling model training, the accuracy of this method is 3.89% higher than that of the supervised method in the Khmer sentence-level sentiment classification task when the artificially labeled data is the same. [ABSTRACT FROM AUTHOR]