Construction and Practice of a Standard LLM System for Electrical Apparatus Based on Residual Network Algorithm Feature Extraction

Standard retrieval in the electrical apparatus field faces challenges such as the fragmentation of multimodal information and insufficient semantic understanding. This paper proposes a layered processing architecture: First, a keyword indexing system is built based on the TF-IDF and TextRank algorithms to achieve the batch annotation of core terms in standard documents. Then, the Llama3 system is locally deployed, integrating text, image, and video features. The experiments show that, compared to traditional pure text model retrieval, Precision significantly improves when handling moderate-sized datasets. The system’s F1 score reaches 91.0% with a dataset of 50 documents, significantly addressing the semantic gap problem in traditional methods. It provides a practical intelligent solution for industrial standardization.