ABSTRACT

This study focuses on the development of a residential property management system integrated with data analysis capabilities tailored for the Qunsheng Dazhi Hui residential community in Huaxi District, Guiyang City. It addresses critical challenges faced by community management, including inefficiencies in manual operations, access and security concerns, lack of centralized data, and the inability to perform comprehensive data analysis. Utilizing algorithms such as K-means clustering and Kohonen neural networks, the system enables targeted strategies for service enhancement and predictive modeling of owner behaviors. Compliance with ISO/IEC 25010 Software Quality Standards was assessed, revealing a high degree of adherence across criteria such as functionality, performance, and security. Feedback from IT experts highlighted areas for future enhancements, such as improved material inventory warnings and online payment features. The findings underscore the potential of the developed system to significantly improve management efficiency, streamline operations, and enhance resident satisfaction, while also providing a valuable reference point for future research in residential property management systems. Keywords: Residential property management system, ISO/IEC 25010 Software Quality Standards, K-means clustering and Kohonen neural networks https://doi.org/10.57180/emqi6814