Application of BP Neural Network Algorithm in Classification of Red Wine Grades

The rapid development of society promotes the improvement of people’s living standard. Red wine has entered thousands of households, at the same time, high-quality red wine is more and more loved by people. As an important standard to distinguish the quality of red wine, the grade of red wine plays an important reference role in the evaluation of red wine. Therefore, accurate and efficient classification of red wine grade is particularly important. In this study, the BP neural network model is established, and the MIV algorithm is introduced to screen the chemical properties of red wine, further optimizing the BP neural network. The optimized BP neural network is applied to the classification of red wine grades to complete the efficient classification of red wine grades. The experimental results show that the correct rate of red wine classification can be effectively improved by using the red wine data screened through MIV algorithm. This meets the requirements of accuracy in classification of red wine grades.