The combine model of the RFM model and K-means is used to classify customer value and AHP method is mostly used to determine the weight of indicators, without considering the relationship between the indicators of RFM model. In this study, firstly, we select the average time interval, the customer purchase frequency in a period of time, average transaction money of each order, and customer active time to structure RFMT model in order to measure the customer value. Then, determine the index weight by using grey correlation degree. Finally, aiming at the shortcomings of K-means, K-means ++ and elbow law are used to carry out cluster analysis of RFMT model. This model can make a more detailed division of customer base. It can help e-commerce enterprises to identify the customers that need to be focused on. Meanwhile, the enterprise customers can be divided into customer groups with high value to low value, and put forward specific marketing suggestions for different customer groups.