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.

本文基于数据挖掘技术，结合贝叶斯网络对生态系统数据进行检验，以研究退耕还林和生态补偿的静态和动态效益。建议限制网络结构以降低训练成本并简化模型结构。同时，为了提高单个模型的预测精度，利用集成学习来训练多个模型来解决同一问题。进而基于数据挖掘，探讨生态系统的发展目的、构成要素和静态框架，阐明其运行演化机制，构建退耕还林与生态补偿评价体系。最后，本文结合现状来确定静态和动态收益，然后通过实验和数理统计对其进行系统验证。研究结果表明，本文构建的框架的影响符合可用于实践的构建模型的标准。