A method for online shopping preference analysis based on MapReduce is proposed in this paper. The campus network traffic is analysed using MapReduce model, in which the features of users online shopping behavior is extracted by four MapReduce jobs combined with deep packet inspection (DPI). Making use of those features occuring in different E-commercial websites and with the help of the product information database established by a web crawler, user online shopping conversion rates of E-commercial websites and category of purchased product are analysed and preference analysis results are presented.