Addressing the Land-Sea Coordination plan and the development of resilient cities, the systematic identification and dynamic evaluation of damaged coastal landscape areas have increasingly emerged as critical concerns for the high-quality advancement of coastal city landscapes in China. Damaged space, which exhibits clear signs of problems such as reduced function, weakened structure, and lower health in coastal landscapes, is the primary target for assessing the quality and strength of urban coastal areas. In recent years, mechanisms for evaluating environmental quality, exemplified by Urban Physical Examination, have been progressively and comprehensively refined. These identified mechanisms may offer technical support for the identification, assessment, and management of degraded landscape areas and facilitate the transition of coastal landscape research from static evaluation to systematic diagnosis and targeted governance. Despite the rapid advancement of spatial analysis and prediction technologies, it is of paramount importance to develop quantitative, parameterized methods and tools tailored to the needs of damaged landscapes. This development is essential for the precise identification, characterization, and analysis of coastal landscape damage, as well as for effectively interpreting driving mechanisms and ultimately improving the efficacy of decision-making in urban coastal landscape regulation strategies. This study addresses current challenges in research on damaged coastal landscapes, including insufficient systematic analysis, incomplete identification systems, low simulation accuracy, and unclear regulatory strategies. This study integrates landscape resilience theory with existing research in landscape ecology. The researcher concentrates explicitly on elucidating the dynamic evolution patterns inherent in coastal city landscapes and seeks to establish a robust technical framework for the identification and representation of damaged coastal landscape spaces. Regarding the research methodology, the PLUS model is adopted to develop a dynamic simulation and representation analysis system that is specifically designed for landscape degradation. By leveraging GIS platforms to integrate land-use transfer matrix analysis and spatial autocorrelation techniques, this study distills core degradation trends and reveals spatial aggregation patterns. Additionally, it incorporates both single-factor and interaction-detection algorithms from geographic detector models to conduct precise identification, dynamic simulation, and mechanistic analysis of degradation spaces within urban coastal landscapes. In terms of empirical investigation, the researcher specifically selected the coastal zone of Yancheng City in Jiangsu Province to conduct a representative case study. Specifically, landscape-type data spanning 2010-2020, together with a comprehensive database of natural and socio-economic driving factors, were used for the assessment. The PLUS model and geographic detector were employed to simulate and analyze the evolutionary trajectories and spatial clustering characteristics of degraded areas. The analytical process then examined the pathways of landscape transformation and assessed the impacts of various driving processes. The empirical findings of this study reveal that damaged landscape areas within the study region exhibit a general spatial pattern characterized by aggregation along the coastline and expansion toward the interior. Specific locales, including coastal wetlands, reclaimed land areas, and the peripheries of urban expansion zones, are identified as notably high-value clusters of landscape degradation. Among natural landscape types, forests and wetlands have undergone considerable damage. The results from factor detection indicate that vegetation cover scale, climatic temperature conditions, population density, and land-use changes are the primary determinants of the evolution of degraded areas. These factors demonstrate not only significant individual explanatory power but also notable synergistic effects and interactive influences. Consequently, restoration and rehabilitation initiatives should be executed in a scientifically coordinated manner. In addition, strategic focus should be directed towards the multi-level governance of affected landscape areas and the integrated management of the principal driving variables and their interaction mechanisms. This paper presents a methodological framework for identifying and representing damaged coastal landscapes using the PLUS model. This framework allows researchers to perform accurate detection and simulation of damaged areas by integrating multi-source spatiotemporal data. Apart from that, the proposed approach offers robust technical support for the systematic diagnosis and strategic spatial planning of urban coastal landscapes. Furthermore, this paper provides a replicable methodological reference for future practices in landscape assessment, monitoring, and regulatory decision-making. The study results can also offer contributions to the broader goals of sustainable coastal zone management and resilient urban development.