Based on the cumulative prospect theory, a reward punishment [−1, 1] operator and a grey correlation decision coefficient were introduced to deal with the selection of multiple retailer evaluation indexes, and a new prospect value function was obtained. The comprehensive prospect value of each retailer was calculated to rank and compare with that under the expected utility value. The example shows that the comprehensive prospect value can better reflect the real evaluation of users. Users prefer retailers who offer multiple measures in a service contract. A certain consumer incentive can improve the satisfaction of service evaluation of retailers, and make up for the loss of users caused by imperfect technology and lack of service attitude.