Abstract: With the rapid development of the Internet and mobile application platforms, massive user data has been generated by mobile applications. Such data has become an important data source for accurately analyzing user requirements preference. Many researchers have analyzed and mined user requirements preference from user data. However, the existing studies do not link the multi-dimensional information of mobile applications, and only explore the characteristics of a few dimensions of the data. In this paper, we propose a method to analyze user requirements preferences based on meta-path embedding, which can personally recommend mobile applications for users. Specifically, we first analyze the semantic topics in the text information of mobile applications, which enriches the analysis dimension of user requirements preferences. Second, we construct a conceptual model that integrates multi-dimensional information for mobile applications, including multi-dimensional data that affects user choices. Based on the conceptual model, we design a series of meaningful meta-paths to accurately capture the semantics of user requirements preferences. Finally, we analyze user preferences based on the meta-path embedding technique to recommend personalized mobile applications for users. In this paper, we use the real data set obtained from the Apple App Store to evaluate our model, which contains 1507 mobile applications and 153501 user reviews. The experimental results show that our method outperforms the existing models in all metrics, in which the average F1-measure increases by 0.02, and the average NDCG increases by 0.1.