Comprehensive Evaluation of Ecological Civilization Construction Level and Social Governance Efficiency by Big Data Algorithm Analysis
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Abstract
Accurate assessment of complex urban systems requires efficient acquisition, integration, and analysis of large-scale heterogeneous data. This study proposes a big-data-driven evaluation framework for multi-dimensional performance assessment by integrating parallel computing and the analytic hierarchy process (AHP). A distributed information-processing architecture is developed to collect and fuse data from environmental monitoring, economic development, public services, transportation systems, and governance-related platforms. To improve evaluation efficiency, a parallel computing strategy is employed to process large-scale datasets and construct weighted multi-indicator representations. The proposed framework further incorporates hierarchical decision analysis to quantify interactions among ecological, economic, social, and governance factors. Experimental evaluation using two representative cities demonstrates that the proposed method achieves comprehensive assessment scores of 76.7 and 80.6, respectively, improving evaluation performance by 12.5 and 12.9 points compared with conventional approaches. By combining multi-source information fusion, distributed data processing, and intelligent decision analysis, the proposed framework provides an effective methodology for large-scale monitoring, dynamic system evaluation, and data-driven decision support in complex networked environments. Furthermore, the framework offers a scalable information-fusion and data-processing architecture for distributed monitoring systems, providing methodological references for intelligent sensing, networked information acquisition, and data-driven assessment in complex communication and infrastructure environments.
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