Assessment of digital development design model using deep learning neural network

Abstract

In the context of China’s cultural and tourism industry, cultural equipment plays a critical role in cultural dissemination, especially in remote areas with harsh road conditions and unique environmental factors. However, the efficiency and stability of manual analysis are significantly challenged by these conditions and the vast yet sparsely collected monitoring data. This study aims to develop a method for extracting valuable information from monitoring data to assess the health status of cultural equipment. We introduce a deep learning-based algorithm that leverages convolutional neural networks (CNNs) to extract local features from multidimensional monitoring indicators and long short-term memory (LSTM) networks to capture time series features, facilitating the classification of cultural equipment’s health status. The algorithm’s effectiveness is demonstrated through simulation results, highlighting its practicality and applicability in real-world scenarios. This research not only provides a novel approach for cultural equipment health assessment but also contributes significantly to the field by addressing the challenges of data analysis in complex environments, underscoring the importance of technological advancements in preserving cultural heritage.