Modeling of natural language processing technology in front-end intelligent interaction

In this paper, a unified model of front-end intelligent Interaction (FII-UM) based on natural language processing technology is proposed to solve the integrated challenges of multi-modal data fusion, semantic understanding and real-time generation in front-end intelligent interaction. The model constructs an end-to-end intelligent interactive system through four innovative modules: multi-modal input joint coding, knowledge-enhanced semantic understanding, lightweight generation and real-time decision making, continuous learning and privacy protection. In the multi-modal input joint coding module, FII-UM adopts hierarchical cross-modal coding framework, dynamic time warping (DTW) algorithm and cross-attention mechanism. The knowledge-enhanced semantic understanding module is deeply integrated with structured knowledge graph and pre-trained language model. Dynamic knowledge injection mechanism and multi-hop inference engine further enhance the performance of the model in complex query processing. Lightweight generation and real-time decision module aiming at the hardware limitation of mobile devices, hierarchical generation architecture and joint optimization strategy are proposed. The experimental results of knowledge distillation compression algorithm and reinforcement learning optimization framework show that FII-UM is superior to the baseline model in multi-modal retrieval, noise robustness, multi-hop reasoning, lightweight generation efficiency and real-time performance.