Research on Collaborative Classification of E-Commerce Multi-attribute Data Based on Weighted Association Rule Model

Because the association between multi-attribute data of e-commerce is not obvious, the traditional collaborative classification method of e-commerce multi-attribute data has the problem of low classification accuracy. Therefore, the weighted association rule model is introduced to realize the optimal design of collaborative classification method of e-commerce multi-attribute data. Firstly, the weighted association rule model is built, and the multi-attribute data is mined and cleaned under the e-commerce platform. Taking the processed e-commerce data as the sample, the multi-attribute data classification index of e-commerce is determined. Through setting project weight, e-commerce data attributes and calculating multi-attribute relevance, multi-attribute data collaborative classifier is obtained. In the weighted association rule model, the collaborative classifier is used to get the multi-attribute data collaborative classification results of e-commerce. Compared with the traditional collaborative data classification methods, it is concluded that the accuracy of collaborative data classification is improved under the e-commerce platform of clothing and food 24.22%.