In the era of the Internet and big data, online user reviews have become a crucial source for extracting product requirements. However, these reviews are often characterized by short text length, diversity, personalization, and redundancy, making traditional requirement modeling approaches ineffective in accurately identifying and mapping user requirements to product functionalities. To address this challenge, we propose LLM-SBERT Requirement Mapping (LSRM), an automated method that enhances requirement extraction accuracy. LSRM leverages a Large Language Model (LLM) with Chain-of-Thought (CoT) reasoning to generate representative keywords for user requirements. These keywords, along with the original text, are embedded into a unified semantic space using Sentence-Bidirectional Encoder Representations from Transformers (SBERT). By applying vector concatenation and semantic similarity calculations, LSRM precisely maps diverse user requirements to standardized product functionalities. We evaluate LSRM using real-world user review data from the electric vehicle industry. Experimental results demonstrate that LSRM significantly outperforms traditional rule-based and machine learning-based approaches in terms of accuracy, precision, recall, and F1-score for requirement identification and mapping. This method presents a novel approach to automated requirement engineering, enhancing product development responsiveness and fostering user-centric innovation.