Intelligent policy framework: Natural resource conservation, knowledge and big data analytics

In today's context of escalating environmental pressure, traditional methods of natural resource conservation face numerous challenges. The use of big data analytics to support the formulation of environmental policies has become a crucial approach for enhancing the efficiency and scientific basis of these policies. To enhance the reliability of the results, this study employed two algorithms: Bayesian inference and a weighted Support Vector Machine (SVM) algorithm based on the grey relational analysis. Bayesian inference constructs a conditional probability network to model and analyze complex relationships in a multi-factor environment, allowing for dynamic updates of the influences of various factors and providing precise evaluations of natural resource protection policies. This approach integrates prior information and observational data to ensure the continuity and accuracy of predictions. The weighted SVM algorithm based on grey relational analysis improves the accuracy of the predictive model by identifying key factors within multi-dimensional data and assigning appropriate weights to different features to address the challenges posed by incomplete or noisy data. By combining these two methods, this study effectively handled complex data and interactions while enhancing prediction accuracy, thereby providing reliable data support and a scientific basis for policy formulation and adjustment. The study revealed that these methods not only effectively predict and assess the impact of policies, but also provide policymakers with real-time data support, enabling more precise decision-making. Although shortcomings remain in data processing and policy prediction accuracy, the methods proposed in this study offer new ideas and tools for addressing these issues.