## 5 Application of Data Analysis Algorithm in Urban Real
### The Application of Deep-Learning Algorithm in Real Estate Data  
As one of the core technologies in the field of artificial intelligence, the deep-learning algorithm is a method of simulating the structure and function of the human brain through multi-layer artificial neural networks, aiming to analyze, understand, and generate complex data [55]. As a deep-learning algorithm, convolutional neural network (CNN) has achieved remarkable results in the field of image processing, and its application in real estate image analysis has gradually shown its unique advantages [56]. Real estate image analysis mainly includes real estate image recognition, land use classification, building damage assessment, etc. CNN provides powerful technical support for real estate management and urban optimization by extracting features and classifying these images. First of all, in terms of real estate image recognition, CNN can automatically extract features in real estate images, such as architectural style, floor height, surrounding environment, etc., to achieve accurate identification of property types. The application of this technology can help improve the efficiency of real estate registration, evaluation, and transactions and provide more accurate data support for the real estate market. Secondly, land use classification is an important link in urban planning and land resource management. Using CNN to classify remote sensing images can quickly and accurately identify various land use types, such as residential, commercial, industrial, greening, etc. [57]. This helps government departments formulate reasonable land use policies and optimize urban spatial layout. In addition, CNN also has broad application prospects in building damage assessment. By analyzing building images after natural disasters such as earthquakes and floods, CNN can identify the degree of damage to buildings and provide strong support for post-disaster rescue and reconstruction. The application of this technology can help improve rescue efficiency and reduce disaster losses. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
In the process of real estate image analysis, the core advantages of CNN are reflected in the following aspects:  
1. Feature extraction capability: CNN can automatically extract local features in the image through the combination of convolutional layers and pooling layers and gradually abstract higher-level features, thereby improving the accuracy and robustness of image analysis. 2. Parameter sharing: The weight-sharing strategy in CNN greatly reduces the number of model parameters, reduces the computational complexity, and improves the operation speed. 3. Applicable to complex scenarios: CNN can process complex image data, such as deformation, rotation, scaling, etc., which gives it a wide range of potential applications in real estate image analysis. 4. End-to-end learning: CNN adopts an end-to-end learning method, which avoids the cumbersome features of engineering in traditional image analysis and improves the analysis efficiency. As shown in Table 6, the application of convolutional neural network (CNN) in real estate image analysis has significant advantages, providing strong support for real estate management and urban optimization [30]. With the continuous advancement of deep-learning technology, CNN will be more widely used in the field of real estate image analysis in the future, making greater contributions to the construction of smart cities. To ensure the privacy and security of citizens within these applications, we have integrated stringent data protection measures [29]. These include the use of anonymization techniques to preprocess images, ensuring that no personal ID information is extracted or stored [31]. We also focus on analyzing public spaces and aggregated data, which do not compromise individual privacy. Furthermore, the deployment of these algorithms is subject to regulatory oversight to prevent misuse and to uphold ethical standards in data processing.