随着信息技术的发展, 采集、存储和管理数据的手段日益完善, 数据挖掘学科应运而生。针对作物蒸散量()计算中参数多、噪声复杂和非线性的特点，本文研究一种数据挖掘方法——随机森林，在数据挖掘建模过程中引入知识流的概念，通过知识流模型的分层，实现从不同角度、不同层次观察模型知识流的分布和流动情况,抓住建模关键环节,为更加准确、高效地建立模型提供支持。将其应用于作物蒸散量计算中，以西安的气象数据为输入参数，以FAO56 Penman-Monteith公式计算的结果作为预测对比值，进行了建模与验证。结果表明，采用知识流建立的数据挖掘模型预测精度较高、稳定性好、泛化能力强，能够有效预测作物蒸散量。相比其他模型效率高，性能更优，尤其适用于大样本的作物蒸散量预测分析中，对于确定作物需水量有一定的参考价值。

The discipline of data mining emerges with the development of information technology and maturation of methods of data collection,storage and management. For crop evapotranspiration (ET0 )parameter calculation has more complex and nonlinear noise characteristics,a data mining method named random forest is researched. The data mining modeling process introduced the concept called knowledge flow,through hierarchical knowledge flow model,checking knowledge flow distribution and flow situation and seizing the key link by different aspects and levels were carried out. It provided more accurate and efficient support for modeling. It will be applied to the calculation of crop evapotranspiration and verified by building a model which takes Xian weather data as input parameters and contrast it with the results from FAO56 Penman-Monteith formula. The result shows that the data mining model based on knowledge flow is of high accuracy,good stability and strong generalization ability. It can effectively predict the crop evapotranspiration. Compared to other models,it has higher efficiency and better performance,especially applicable to the prediction of large samples＇ crop evapotranspiration. It also has certain reference value for judging crop water requirements.