UI animations, such as card movement and menu slide in/out, provide appealing user experience and enhance the usability of mobile applications. In the process of UI animation implementation, it is difficult for developers to identify suitable APIs for the animation to be implemented from a large number of APIs. Fortunately, the huge app market contains millions of apps, and they can provide valuable data resources for solving this problem. By summarizing the API usage for the same or similar animations in apps, reusable knowledge can be mined for the API recommendation. In this paper, we propose a novel method Animation2API, which mines the knowledge about APIs from existing apps and recommends APIs for UI animations. Different from existing text-based API recommendation approaches, Animation2API takes the UI animation in GIF/video format as query input. Firstly, we construct a database containing mappings between UI animations and APIs by analyzing a broad set of apps. Then, we build a UI animation feature extractor, which can be used to gain temporal-spatial feature vectors of UI animations. By comparing the temporal-spatial feature vectors between UI animations, we identify animations that are similar to the query animation from the database. Finally, we summarize the APIs used for implementing these animations and recommend a list of APIs for developers. The empirical evaluation results show that our method can achieve 82.66%Success rateand outperform the baseline Guru by 230.77% and 184.95% in terms ofPrecisionandRecallwhen considering twenty APIs. In the user study, we take the scenarios of using web search and ChatGPT to implement animations as baselines, and the results show that participants can complete animations faster (14.54%) after using Animation2API. Furthermore, participants’ positive feedbacks on the questionnaire indicate the usefulness of Animation2API.