Semantic Sensor Analysis in <scp>MOOC</scp> System via Deep Learning

ABSTRACT With the application of Internet of Things (IoT) technology, the field of online education has developed rapidly. Online users can flexibly obtain learning resources on the MOOC learning platform and conduct online course learning. The development of IoT enables various systems with sensing functions to continuously access the Internet. The interconnected data makes the development of education tend to be digital and intelligent. Semantic sensor associates semantic technology with a large number of sensors in IoT, providing effective technical means for data representation, management, and sharing. It can provide a theoretical basis for knowledge‐based intelligent semantic sensor data processing in IoT. This paper analyzes conventional deep learning algorithms, that is, connectionist text proposal network (CTPN) and convolutional recurrent neural network (CRNN). Then we combine CTPN and CRNN to propose the improved algorithm. The improved algorithm can more accurately recognize text and be used in IoT to extract spatial features in semantics. It can solve contextual relationships between texts, capture temporal features, effectively handle the complexity and diversity of semantic sensor data in the MOOC system. Finally, compared with the traditional algorithms, the improved algorithm achieves higher accuracy and faster recognition speed.