[Objective] This study aims to solve problems such as text feature extraction bias and difficult separation of ambiguous semantics caused by the unique expressions and semantic drift phenomenon in Chinese. [Methods] This paper proposes a supervised contrastive learning semantic optimization method, which first uses a pre-trained model to generate semantic vectors, then designs a supervised joint self-supervised method to construct contrastive sample pairs, and finally constructs a supervised contrastive loss for semantic space measurement and optimization. [Results] On the ChnSentiCorp dataset, the five mainstream neural network models optimized by this method achieved F1 value improvements of 2.77%-3.82%. [Limitations] Limited by hardware resources, a larger number of contrastive learning sample pairs were not constructed. [Conclusions] The semantic optimization method can effectively solve problems such as text feature extraction bias and difficult separation of ambiguous semantics, and provide new research ideas for text sentiment analysis tasks.