Parameter Optimized Intelligent Algorithm for Consumer Psychology of Web Red Milk Tea Purchases

With the continuous progress and development of Internet and information technology, online milk tea has gradually entered the public eye. The consumers' consumption psychology in online milk tea consumption decision-making is widely valued by practitioners in milk tea industry. How to effectively analyze consumers' psychological decision-making process has become the main topic at present. Taking Fuzhou City as an example, this study uses IBM SPSS20.0 software to test the reliability and validity of the questionnaire. Seven explanatory variables and one target variable were determined by principal component analysis (PCA). Four algorithms, decision tree (DT), random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN), were used to establish the relationship between target variables and explanatory variables, and the consumer psychology of online milk tea was analyzed. Intelligent algorithm is used to combine simulated annealing (SA) algorithm with SVM to realize automatic parameter adjustment and improve classification accuracy. The experimental results show that the decision tree obtains six decision rules that affect consumers' psychology of buying online milk tea, which can be used as an important decision to judge whether consumers agree to buy online milk tea, and help the milk tea industry to establish a better foothold and provide valuable reference.