Consumer purchasing intention and marketing data mining model in the digital economy

In recent years, there has been a rapid development of science and technology in China. This article uses a marketing data mining model to conduct research on consumers' purchasing intention. The research is based on traditional questionnaire surveys, further Bayesian inference and shopping basket analysis, to analyse how to enhance buyers' desire in what environment they would enhance their purchase desire. Through shopping basket analysis and Bayesian inference, this article found that the purchase information, purchase demand, and purchase behaviour of the buyer could obtain the purchasing intention of the buyer, which can also formulate reasonable marketing strategies for businesses. In the product analysis conducted in this article, it can be found that the highest number of purchases of vegetables and fruits was 14,192. Based on similar analysis, the correlation degree between vegetables and fruits in small categories was 0.800. The article made a reasonable analysis of the two and concluded that putting vegetables and fruits together for sale was the best choice. This article also made a correlation analysis for other products that were analysed as having a high number of purchases. It was believed that the higher the correlation between the two products, the higher the sales volume would be.