深度学习是近年来机器学习领域的一个热点研究方向，其主要方法是通过增加学习器的层数，增大其通道数 和参数的规模，借助大数据学习时代的超强计算能力，发现原始数据集中的高层抽象概念，为应用领域的决策支持服务。探讨 了在信息系统的数据分析任务中深度学习技术的应用方法，着重阐述了卷积神经网络和堆叠自动编码器的主要原理和实现方 法，及其在信息系统的数据分析中的应用案例，并对其应用价值进行了分析。

Deep learing is an active research area in machine learning community. Its main idea is to discover high-level abstract concepts in original datasets with huge computational power of the age of big data, by increasing the number of layers of the learners, so as to increase the size of channels and the quantity of parameters. It becomes a significant information source for decision support of application domains. We explore the methods of applying deep learning technologies in the data analysis tasks of information systems by presenting the main principles and implemetation details of two deep learning models, convolutionan neural network and stacked auto-encoders in emphasis, their application cases in the data analysis of information system, as well as the analysis on their application value.