Method and device for identifying a risk merchant

A system comprising one or more processors and one or more non-transitory computer-readable memories coupled to the one or more processors and configured with instructions executable by the one or more processors to cause the system to perform operations comprising:

• collecting user data of a plurality of merchants;
• training a classification model using the user data of the plurality of merchants, wherein the classification model is used to mark each merchant in at least one merchant cluster with a corresponding risk category;
• determining, for each of the at least one marked merchant cluster based on a user correlation degree of a merchant to be identified and the each marked merchant cluster, a degree of similarity between the merchant to be identified and the each marked merchant cluster, wherein:
• the user correlation degree of the merchant to be identified and the each marked merchant cluster is based on a user correlation coefficient of the merchant to be identified and the each marked merchant cluster and a number of same users of the merchant to be identified and the each marked merchant cluster;
• and
• determining a corresponding risk category for the merchant to be identified based on the degree of similarity between the merchant to be identified and the each marked merchant cluster.

One or more non-transitory computer-readable storage media storing instructions executable by one or more processors, wherein execution of the instructions causes the one or more processors to perform operations comprising:

• collecting user data of a plurality of merchants;
• training a classification model using the user data of the plurality of merchants, wherein the classification model is used to mark each merchant in at least one merchant cluster with a corresponding risk category;
• determining, for each of the at least one marked merchant cluster based on a user correlation degree of a merchant to be identified and the each marked merchant cluster, a degree of similarity between the merchant to be identified and the each marked merchant cluster, wherein:
• the user correlation degree of the merchant to be identified and the each marked merchant cluster is based on a user correlation coefficient of the merchant to be identified and the each marked merchant cluster and a number of same users of the merchant to be identified and the each marked merchant cluster;
• and
• determining a corresponding risk category for the merchant to be identified based on the degree of similarity between the merchant to be identified and the each marked merchant cluster.