In the field of industrial Internet of Things （IoT）， device entity discovery constitutes a critical component of device management. Compared to intelligent sensors， the discovery process of non-intelligent sensors is particularly complex due to their lack of inherent communication protocols， making efficient and accurate recognition of non-intelligent devices a technical challenge urgent to be solved. Therefore， a knowledge graph and Large Language Model （LLM）-based efficient recognition method for non-intelligent sensors was proposed. Firstly， a three-layer knowledge graph was constructed by extracting attribute values from non-intelligent sensors. Secondly， the feature vectors of sensors were extracted from the knowledge graph. Finally， the feature vector information was fed into LLM for fine-tuning， and the optimal fine-tuning parameters for the model were obtained through optimization via a series of experiments. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 96.2% on the public IoT sensor dataset SensorData， enhancing recognition efficiency significantly.