Development and Sharing of Traditional Cultural and Educational Resources Based on the Internet of Things

Integrating traditional, cultural and educational resources into modern learning platforms has become a resilient need in an increasingly globalized and digitally connected world. While technology continues to evolve at a rapid pace, the preservation and dissemination of cultural heritage remain crucial for maintaining the unique identities of societies. Research explores the integration of internet of things (IoT) technologies and advanced deep learning (DL) methods to manage and distribute data that include digital representations of artifacts, historical sites, and cultural practices. Data including location, environmental conditions, and user interactions, are collected via IoT devices to enhance the learning experience. Pre-processing techniques, such as median filtering and standardization, are applied to clean and normalize the data, ensuring its quality and consistency. Feature extraction methods, including discrete wavelet transform (DWT) and principal component analysis (PCA), are employed to capture the most relevant characteristics of the cultural data. The novel refined nutcracker optimized deep neural network (RNO-DNN) model is designed to intelligently analyze the pre-processed data, enabling personalized learning and immersive cultural experiences. The RNO-DNN enhances the accuracy of content recommendations and optimizes the system’s performance in real-time interactions. It also addresses challenges such as data privacy, computational efficiency, and the technical infrastructure required for RNO-DNN integration. The findings highlight the potential of combining IoT with DL to revolutionize the accessibility, engagement, and preservation of traditional cultural content, making it a critical tool for modern education and cultural heritage management.