Data mining for evaluating the ecological compensation, static and dynamic benefits of returning farmland to forest

Based on data mining technology, this paper incorporates Bayesian networks to examine ecosystem data in order to investigate the static and dynamic benefits of returning farmland to forests and ecological compensation. The restricted network structure is suggested to reduce training costs and simplify model structure. Simultaneously, in order to increase prediction accuracy over a single model, ensemble learning is utilized to train multiple models to solve the same problem. Furthermore, based on data mining, this article explores the ecosystem's development purpose, constituent elements, and static framework, illustrates its operation and evolution mechanism, and constructs an evaluation system for returning farmland to forest and ecological compensation. Finally, this article incorporates current situation to determine the static and dynamic benefits, and then systematically verifies it using experiments and mathematical statistics. The research findings indicate that the impact of the framework built in this paper meets the standards of the construction model which could be used in practice.