Abstract: The decision tree algorithm is a research hotspot in the field of data mining，which is usually used form classifiers and prediction models.In practice，it is widely used.This paper focuses on classical ID3 algorithm，analyzes its advantages and disadvantages，combines with Taylor and Maclaurin formula，and then puts forward a new attribute selection criterion of decision trees.This modified algorithm improves the classification accuracy，reduces the generation time of decision trees，and shortens the computational cost by the calculation of the simplified information entropy.Experimental results show effectiveness and correctness of the improved algorithm.

摘要： 决策树算法是数据挖掘领域的一个研究热点，通常用来形成分类器和预测模型，在实际中有着广泛的应用。重点阐述了经典的ID3决策树算法，分析了它的优缺点，结合泰勒公式和麦克劳林公式提出了新的属性选择标准。改进后的算法通过简化信息熵的计算，提高了分类准确度，缩短了决策树的生成时间，减少了计算成本。实验证明，改进后算法的有效性和正确性。