Exploration on multimodal data recognition method for Internet of Things based on deep learning

Abstract

With the rapid development of Internet of Things (IoT) technology, a large amount of sensor data, images, voice, and other data are being widely used, bringing new opportunities for intelligent and cross-domain information fusion. Effective feature extraction and accurate recognition remain urgent issues to be addressed. This article explores the application of deep learning (DL) in multimodal data recognition methods of the IoT and proposes path optimization for multimodal data recognition methods of the IoT under DL. This article also provides in-depth analysis and discussion on the optimization of multimodal data recognition models based on DL, as well as specific measures for optimizing the path of multimodal data recognition based on DL. In this paper, the long short-term memory (LSTM) technology is introduced, and the LSTM technology is used to optimize the multi-modal data recognition method. It can be seen from the comparison that the processing efficiency of data analysis, information fusion, speech recognition, and emotion analysis of the multimodal data recognition method optimized by LSTM technology is 0.29, 0.35, 0.31, and 0.24 higher, respectively, than that of data analysis, information fusion, speech recognition, and emotion analysis before optimization. Introducing DL methods in multimodal data recognition of the IoT can effectively improve the effectiveness of data recognition and fusion and achieve higher levels of recognition for speech recognition and sentiment analysis.