Real-Time Legal Advisory System for Mobile Computing Devices via Deep Learning

With the widespread adoption of mobile internet technologies and a growing public awareness of legal rights, demand for real-time and accurate legal advice in mobile environments has increased significantly. Traditional legal services are constrained by uneven resource distribution and delayed response times. Existing legal advisory systems, which often rely on static knowledge bases or simple rule-matching techniques, have demonstrated notable limitations in understanding personalized user needs, processing multimodal inputs, and adapting to mobile devices. Collaborative filtering methods require large-scale annotated datasets and are generally inadequate in capturing the domain-specific semantics of legal texts. Recurrent neural networks (RNNs) and other models impose computational burdens that hinder real-time responsiveness on mobile platforms. Moreover, they lack mechanisms for high-order feature integration and key information extraction in user-legal knowledge interactions. To address these challenges, a real-time legal advisory model based on an attention-compressed interactive network was proposed. By extracting interaction features between user consultation texts and legal knowledge units and integrating an attention mechanism to filter critical semantic information, a lightweight compressed interaction network was designed to enable high-order feature fusion while remaining suitable for devices with limited computational capacity. A score prediction module was incorporated to quantify the relevance of advisory responses, forming an end-to-end recommendation system. This approach overcomes dual bottlenecks in semantic modeling and device adaptability that hinder traditional models, providing a technical solution for generating efficient legal advice in mobile settings. The findings offer practical implications for deploying intelligent legal services on edge devices and contribute to the broader development of accessible legal infrastructure.