In recent years, the recommendation system has become a hot spot in the field of data analysis and mining, as well as information retrieval. However, there are still problems in some recommendation systems serving the multi-interest preferences of users. Firstly, the users’ interests are not single, and the preference for multiple interests is not equal. Secondly, it is not sure whether the users’ current interests will continue in the future. Therefore, this study proposes a MIES algorithm model by utilizing the items that users participate in to generate multiple interests and capture the sustainability of their personalized interests. The model effectively captures users’ diverse latent interests while emphasizing the sustainability of their interests, thus improving the quality of recommendations. Comparative experiments demonstrate that the model effectively addresses the challenges of capturing users’ multidimensional interests in recommendation systems and ensuring the sustainability of personalized interests.