Internet of Things （IoT） device manufacturers often reuse a large number of open-source components compiled from open-source code in firmware development， with each firmware typically comprising hundreds of such components. If these components are not updated promptly， they may carry unpatched vulnerabilities to integrate into the firmware， thereby posing significant security risks to IoT devices. Therefore， identifying binary components in IoT firmware is crucial for ensuring the security of IoT devices. To address the difficulty of the existing methods in identifying binary components on a large scale， a large-scale IoT binary component identification method based on Named Entity Recognition （NER） was proposed. Firstly， internal binary components were extracted from firmware through decompression. Then， semantic information of the component was obtained through two ways： extraction of readable strings and execution of the component. Finally， the RoBERTa-BiLSTM-CRF’s NER model was utilized to identify component names and version numbers. Experimental results on 6 575 firmware samples released by 12 popular IoT manufacturers demonstrate that the proposed method achieves an F1 value of 87.67%， and identifying 163 binary components successfully. It can be seen that this method effectively expands the identification range of binary components in IoT firmware， enhancing firmware security from the perspective of software supply chain.