The construction of high-quality urban communities holds significant innovative importance for the urbanization process aligned with China’s modernization goals. Among the core dimensions of such development, the integration of economic, social, and environmental factors is essential. Traditional community renewal studies often treat these dimensions in isolation, lacking a systematic understanding of their dynamic synergy. This study addresses this gap by proposing a multidimensional collaborative framework based on the High-quality System Integration Model (HSIM). The framework follows a three-stage structure— “element identification, measurement analysis, and technical evaluation”—to achieve the dynamic coordination of economic, social, environmental, spatial, and technological dimensions in urban community renewal. Theoretically, this study advances the existing literature by providing a global collaborative framework that integrates five core dimensions: engineering construction, ecological environment, spatial function, socio-economy, and energy low-carbon. It moves beyond static indicator systems by incorporating dynamic weighting mechanisms and empirically examining how technological optimization drives structural shifts in dimension priorities. Methodologically, the study employs factor analysis and dynamic weighting based on panel data from the Binhai New Area in Tianjin. A total of 20 secondary indicators were selected across the five dimensions, following international standards such as the Dow Jones Sustainability Index (DJSI) and ESG frameworks, as well as domestic guidelines. Data were collected from multiple sources, including planning documents, statistical yearbooks, and spatial analyses using Python-based data extraction and spatial syntax methods. Empirical analysis was conducted across ten functional zones in the Binhai community, including residential areas, eco-parks, cultural tourism zones, industrial parks, and central business districts. The KMO measure (0.521) and Bartlett’s test of sphericity (p < 0.001) confirmed the suitability of the data for factor analysis. The first five principal components accounted for 97.487% of the total variance, leading to the identification of five key technical factors: engineering construction, ecological environment, spatial function, socio-economy, and energy low-carbon. Factor scores were calculated for each functional zone, revealing significant variation in performance across dimensions.The findings reveal several key insights. Firstly, the study demonstrates that under controlled development intensity, dynamic optimization of factor measurements leads to a 19.6% improvement in the comprehensive performance of the planning scheme. Notably, the contribution weights of the engineering construction and ecological environment dimensions increased by 21.3% and 18.7%, respectively, indicating a structural shift from growth-oriented to quality-oriented renewal logic. Second, key performance indicators showed substantial improvements: regional carbon emission intensity decreased by 23.8%, while average public service accessibility increasedby 31.5%. These results validate the effectiveness of the HSIM framework in enabling measurable and comparable outcomes. Third, the factor score rankings across functional zones reveal differentiated synergy pathways. For instance, the Blue Economic Core Area ranked highest in comprehensive performance (score: 1.203), excelling in engineering construction and socio-economic factors, while the China-Singapore Eco-Demonstration Base performed best in ecological environment and low-carbon energy factors. In contrast, areas such as the Tanggu Leisure Experience Zone exhibited low scores across all dimensions, highlighting the need for targeted interventions rather than one-size-fits-all strategies. The study makes several theoretical and practical contributions. Theoretically, it advances urban renewal research from a static, dimension-listing approach to a dynamic “synergy dynamics” paradigm. By quantifying the shifting weights of dimensions under technological optimization, the study provides an empirically grounded model of systemic transformation in high-quality community development. Practically, the HSIM framework offers a replicable decision-support tool for planners. It enables the diagnosis of synergy bottlenecks, the prioritization of dimension-specific interventions, and the simulation of performance outcomes under alternative design scenarios. In the Binhai case, the final optimized planning scheme achieved a 23.8% reduction in carbon intensity and a 31.5% increase in service accessibility, demonstrating the operational value of the framework.In conclusion, this study confirms that multidimensional synergy in urban community renewal is not a static configuration but a dynamic process driven by technological optimization. The HSIM framework provides both a theoretical lens and a practical methodology for understanding and guiding this process. Future research may extend the model to diverse geographic and institutional contexts, integrate it with urban information modeling (CIM) and national spatial planning systems, and explore its potential for real-time simulation and adaptive governance in smart city development.