Recommendation system is a tool to automatically find information that users may be interested in from a large amount of information. How to get closer to users' preferences, satisfy users' long-term inherent preferences, and simultaneously take into account users' short-term interest focus changes is an everlasting research problem of recommendation systems. In addition, in order to improve the recommended performance when designing the system, we not only focus on user modeling optimization, recommendation object modeling optimization or recommendation algorithm optimization, but also need to systematically study the recommendation system as a whole, focusing on system fluency and scalability. To solve these problems, this study designs a recommendation system that combines real-time recommendation with offline recommend, and proposes a method to ensure the fluency of the system by using the pool of recommendation data. Based on the analysis of real-time data and historical data, real-time recommendation and offline recommendation are provided, which can fit the long-term preferences of users and adapt to the recent change of interest focus. The control module of the system is used to control and adjust different recommendation result data to improve the scalability of the system. Based on the recommendation system, this study conducts a recommendation experiment for WeChat articles, and evaluates the recommendation effect by analyzing the data in the recommendation pool. The experimental results show that the recommendation data of the system can be gradually close to the user's interest preference.