The current sentiment classification methods often ignore the relative positional features between different words, which makes it difficult for the model to learn the best positional representation of words. To solve this problem, a sentiment classification algorithm based on Gaussian distribution guided position relevance weight is proposed. First, the positional relevance between each word and other words is calculated. Second, the positional relevance is modeled by using an improved Gaussian distribution function, and the results are multiplied with the feature vectors of the words to generate a positional-aware representation of the words. Finally, the algorithm is integrated into the traditional model to verify its effectiveness. The experimental results show that the proposed method obtains higher accuracy than the traditional model, with improvements of 2.98%, 5.02%, and 10.55% in terms of in-domain, out-of-domain, and adversarial evaluation metrics, respectively, indicating its excellent practical value.