Research on the Application of the Internet of Things in Optimizing the Allocation of Ideological and Political Education Resources Using Machine Learning

This paper evaluates the application of the Internet of Things (IoT) in designing smart teaching systems by optimizing ideological and political education (IPE) resources using machine learning (ML). To increase educational reform’s efficacy, we also examine the issues with conventional IPE instruction. This study further develops a cutting-edge, smart framework for teaching ideological and political concepts in the educational setting and changes teaching according to real-life scenarios. Comprising the perception, network, and application stages, the IPE platform is a three-layer IoT architecture. The IoT and internet connections are used by the technology to receive effective IPE instructional activity information in real time. After that, the data are sent over the internet to the data center, where it is used as the initial information for applications, analysis of information, and modeling training. We are able to collect and acquire IPE teaching activity data sets on instructive and educational developments in courses by using IoT devices. The principal component analysis (PCA) approach is used to remove interface characteristics, date, and time elements from the data to increase the evaluation’s correctness. To remove noisy information from the data, the z-score normalization method is used. In addition, this paper suggests a novel efficient Cat Boost method inspired by hunter–prey optimization (AHPO-ECB) for evaluating IPE performance. In this case, the AHPO approach is employed to reduce the misprediction rate of the CB. The suggested model is then used to analyze the predicted performance using a Python program. The results of the experiments indicate that, in comparison to other models currently in use, the suggested AHPO-ECB model performs at the highest level when assessing IPE teaching performance.