Intelligent Control of Building Decoration System Using Convolutional Neural Networks

The comprehensive solution for intelligent regulation of building decoration systems integrates intelligence, automation, and energy conservation, improving the user experience and energy efficiency of buildings, achieving real-time monitoring and automatic adjustment of internal environmental parameters of buildings, and intelligent control of building decoration equipment. The system automatically adjusts the temperature based on indoor and outdoor environmental conditions and user needs, creating a comfortable and healthy indoor environment for users, achieving precise control of internal equipment and systems, improving energy utilization efficiency, and reducing energy consumption. This article utilizes Convolutional Neural Networks (CNN) to achieve the construction of an intelligent control building decoration system. By constructing a suitable CNN model, this paper achieves intelligent monitoring and control of building decoration systems, improves system efficiency and user experience, effectively identifies building decoration status, and achieves precise control based on real-time data. The experimental results show that the optimized data is generally more stable and has a higher accuracy rate, close to 95%, with a smaller fluctuation range. This indicates that the algorithm has better performance after optimization, stronger resistance to random factors, and significantly improved accuracy. This indicates that the algorithm has been improved and stabilized.