With the increasing complexity of mobile applications, existing privacy leak detection methods based on user intent face greater challenges. On the one hand, traditional privacy leak detection, which is based on APP-level user intent, only focuses on whether the privacy collection behavior of the application aligns with its core functional requirements. This approach is not suitable for today’s mobile APP security detection, which has broad functionalities and diverse user intents, necessitating a more fine-grained user intent classification. On the other hand, current research mainly focuses on evaluating whether the privacy collection behaviors triggered by interface widgets, such as icons, are consistent with user intent. However, the improper design and misuse of icons are very common, which limits the effectiveness of privacy risk assessments that rely solely on widget-based user intents. Therefore, a comprehensive understanding of user intent at the overall interface level is still needed. In response to the above issues, this study first extracts and summarizes a fine-grained user intent list suitable for privacy compliance detection based on Chinese privacy policies. Then, based on the characteristics of mobile application interface design, a multi-classification model with multi-modal feature fusion is designed and implemented to identify the user intent reflected by the entire mobile interface. Evaluation results show that the intent extraction tool in this study has achieved 83% in both precision and recall, and the user intent classification model reaches 80% and 83% in precision and recall, respectively, demonstrating good detection effectiveness and practical usability.