Abstract: The quality evaluation of liquor is mainly based on sensory identification, but sensory evaluation is easily affected by various factors such as the body conditions and experience. In order to improve the stability and effectiveness of evaluation, an evaluation method based on physical and chemical indicator data was established. This paper presented a research method based on visual analysis of big data to explore the effects of high-dimensional multivariate indicator data on the sensory characteristics of liquor. First, the correlation between high-dimensional multivariate indicator data and the correlation between data and sensory evaluation was analyzed in the paper. The characteristics of the relationship had been explained through graph models, and built a domain-oriented visual analysis method initially. Then, the data analysis technology based on machine learning was combined with sensory evaluation to build a liquor quality evaluation model. Finally, the importance of indicator parameters was analyzed by combining with the liquor quality evaluation model. On this basis, the evaluation effect of the model was verified. Two types of samples for comparison were selected. One type of sample contained 6 physical and chemical indicators with high importance scores, including density, residual sugar, volatile acidity, alcohol, sulphates and fixed acidity.The other type of sample contained all 11 physical and chemical indicators. The verification results showed that the prediction results of the two types of samples were similar, and the difference in classification prediction error was only 0.4%, indicating that the above 6 physical and chemical indicators were the main components that affect sensory evaluation. It indicated that the visible analysis method could reduce the dimensionality of the physical and chemical indicators, and retain the characteristic information of the liquor quality. This method could be used for the evaluation of liquor quality and played an important role in the scientific evaluation of liquor quality.