Models of Internet of Things Detection System for Translation Based on Artificial Intelligence

Traditional English translation Internet of Things (IoT) detection systems only translate a large amount of text data, ignoring the emotional information of the text data to a certain extent, resulting in poor detection performance of the system. To improve the detection effect, the study first analyzed the characteristics of the English translation of IoT, and then adopted a method based on deep learning. In this method, the TF-IDF (Term Frequency-Inverse Document Frequency) model is combined for text feature extraction. Through this technology, the system can more effectively extract key information from large amounts of text data. The experimental results showed that the model proposed in this article can effectively improve the performance of English translation IoT detection. The system achieved an average recognition accuracy of 88.78% and an average translation sentiment similarity of $\mathbf{8 9. 9 4 \%}$ during the testing process. It can be widely applied in English translation IoT detection systems, providing a certain reference for relevant researchers, thereby improving the efficiency and quality of English translation IoT detection.