In order to extract the semantic and grammatical information of sentences more effectively, this paper proposes a sentence sentiment classification method based on Self-supervised and Self-attention mechanism (SS-SAtt-BiLSTM). In this method, BiLSTM network is used to extract the feature of text context relationship, and self-supervised (SS) learning mode is introduced into the supervised sentence representation model. The sentence itself is used as the label data information of current words, and an improved self-attention mechanism (SA) is used to calculate the attention weight of each moment. The experimental results of MR and Stanford sentient treebank (sst-5) data sets show that this method reduces the dependence on tagged data, and the improved self-attention mechanism enables the model to learn more key features of sentences and improve the classification performance.