This study developed an ecological environment evaluation framework tailored for islands in Jiangsu and validated its applicability using nine representative islands. The evaluation system encompasses 14 indicators across three dimensions: ecological, socio-economic, and policy-climate. By coupling the Time-varying Entropy Weight method with a Bayesian Network, the framework quantifies the dynamic impacts of policy interventions, extreme weather, and human activities. To enhance model accuracy under small-sample conditions, machine learning and deep learning techniques were integrated to construct a multi-layer ensemble evaluation model. The results indicate that this model improves prediction accuracy by 11.3% and reduces the root mean square error by 33.3%. The assessment results reveal significant differences in ecological quality among islands of different types. Natural-type inhabited islands maintain relatively high ecological quality through the synergy of ecological conservation and industrial activity, whereas artificial-type inhabited islands experience significant negative impacts from industrial development. Uninhabited islands generally score around 10, indicating relatively stable ecological conditions but high natural vulnerability. This framework provides a high-precision quantitative approach for dynamic evaluation of island ecological quality under small-sample constraints and offers a scientific basis for customized, island-specific conservation and development management strategies.
Lu et al. (Mon,) studied this question.