In the scenario of mobile edge, load balancing is often used to cache data to reduce the load pressure of the central server. However, this approach only considers the server side and ignores the user needs at the edge of the network. This paper proposes a user-personalized edge data caching strategy. First, the users current preference is analyzed based on the popularity of the content. Then, the users historical preference is mined from the users historical evaluation data of the edge cache content. The two preferences are weighted and combined to transform the edge caching problem into a linear programming problem, so as to cache the users personalized data to the appropriate base station. The experimental results show that the proposed method is superior to other methods in terms of accuracy, indicating that the proposed method is a better edge data caching strategy.