数据挖掘是人工智能和数据库研究的新兴领域, 近年来, 数据挖掘技术的理论与应用研究发展迅速，并引起了国内外研究人员的重视。本文以科学引文索引数据库（SCI）、工程索引数据库（EI）以及清华全文数据库（CNKI）中有关“数据挖掘”研究文章的统计数据为研究基础, 对数据挖掘在我国研究的总体趋势、研究热点、研究分支三个方面进行分析和研究。本文分析了数据挖掘在我国的发展, 并对进一步发展我国数据挖掘的理论研究和实际应用提出了建议。

准确的客户分类是企业有效地实施客户关系管理的基础。基于客户价值的客户分类是根据客户的价值大小来判断客户的类别。本文首先在现有的客户价值评价体系的基础上结合客户生命周期理论设计了一种新的客户价值评价指标体系，从客户的生命周期阶段和客户发展潜力两个方面来评价客户价值。然后根据该指标体系的指标特性，选择神经网络作为评价方法。最后根据客户价值评价结果进行客户分类，并对每个类别的客户特征及其相应的营销策略进行了分析。

本文提出采用人工神经网络的方法对股票级别进行识别，以便辨识股票的优劣。首先建立股票识别的指标体系，对神经网络的训练样本中的各支股票的各项指标进行初步评级，然后用因素状态空间上信息扩散的方法对初步评级的结果进行优化处理，把处理后的结果作为神经网络训练样本的输入和输出因素，并利用BP算法进行网络训练,当计算达到要求的精度后即完成训练。经训练所得的神经网络即可用于识别股票级别。本文最后给出一个实例，具体说明运用信息扩散和人工神经网络的方法识别股票级别的过程，并对结果进行了分析讨论。采用本文所介绍的方法对股票级别进行识别，可以有效地减少主观因素及市场波动所带来的影响。

This paper comparatively analyzes the existing evaluation index of websites, and puts forward the evaluation index and method about the support of a website to enterprise's e-commerce. Through researching on 56 super enterprises of information industry in Sichuan province, throughout China and the world, analyzing and comparatively studying the support ability of a website to an enterprise's e-commerce, this paper brings forward using five levels to categorize the support ability of a website to enterprise's e-commerce. In the end, the flaw of enterprise's e-commerce practice in Sichuan province and corresponding countermeasure will be illustrated.

The information management has become an important base of developing an enterprise's eBusiness. Although our country has spent abundant manpower, material resources and financial support on enterprise information management, most of the information systems were not so good to meet our need and they didn't bring the effect into play sufficiently. Based on the two large questionnaires, this paper has made an initial study and analysis on the status and actual questions of Chinese information management. And then this paper studies the current status of information management of some of the dominant enterprises in Sichuan Province. At last this paper presents some suggestions of intensifying enterprises information management.