Real-time detection of WeChat moments interface blocking behavior and generation of generational user personas based...

With the continuous expansion of user scale on social media platforms, WeChat Moments, as a core social scene for Chinese users, has become a key issue in privacy management and user experience optimization due to the dynamic monitoring of interface blocking behavior and the analysis of generational differences. Current research faces three limitations: Firstly, traditional object detection techniques struggle to meet the real-time requirements of dynamic interface operations, especially in the detection of small targets, resulting in significant accuracy losses. Secondly, existing behavior analysis methods rely on log statistics and questionnaire surveys, lacking the ability to model multimodal interactive behavior in both time and space. Thirdly, user portrait construction is mostly based on static attribute classification, failing to effectively capture the dynamic differences in privacy strategy selection among generational groups. To address these challenges, this study proposes a multimodal collaborative analysis framework that integrates an improved YOLOv5 architecture with dynamic portrait generation. At the methodological level, a three-level collaborative computing architecture is designed. A lightweight YOLOv5-GhostNet model is deployed on mobile devices, achieving cross-modal feature decoupling of text, image, and video blocking behavior through a multi-scale dilated convolution pyramid and dynamic weight fusion mechanism. The detection accuracy reaches 93.2%, an improvement of 8.1% over the baseline model. Secondly, a dynamic threshold algorithm with composite elastic windows is proposed, combining event density perception and dual attenuation factors to reduce the false trigger rate to 4.2%, while simultaneously optimizing the real-time response delay to 105 ms. Furthermore, an orthogonal constrained multimodal fusion strategy is introduced, utilizing KL divergence feature selection and an XGBoost clustering model to construct generational sensitive behavior fingerprints. This reveals behavioral patterns such as Generation Z users preferring fine-grained permission control (58.3% partial visibility) and low complexity in operation paths (single session duration of 1.5 s), forming a significant differentiation from Generation X users' dominant strategy of complete blocking (68.7%). Experiments show that the system maintains a false trigger rate of only 5.1% under adversarial attack scenarios and maintains a clustering accuracy of 83.4% even with 30% data loss. The research conclusion points out that a technical path based on dynamic feature optimization and spatiotemporal correlation modeling can effectively break through the real-time bottleneck of social interface behavior analysis. The generation of generational portraits requires the integration of cross-modal semantic decoupling and incremental learning mechanisms to cope with the coupled effects of user cognitive evolution and system iteration. This achievement provides theoretical support and technical paradigms for the optimization of privacy management strategies on social platforms. Its lightweight architecture design (model parameter count of 1.2 M) and multimodal decoupling method have universal reference value for intelligent human-computer interaction systems.