随着全球的商业竞争愈来愈激烈，客户流失预测已经成为客户关系管理中非常重要的内容。预测即将流失的客户，并制定相应的措施挽留客户已经成为促进企业发展的关键性因素。本文从对电信和信用卡客户的行为数据分析入手，针对其中的冗余特征和正负类样本不均衡等特点，提出一种新的特征选择算法和非均衡数据处理算法，以此建立一种新的客户流失预测模型。(Along with the global business more competitive, customer churn prediction has become the most important content in customer relationship management. Predicting customer retention and formulating corresponding customer churn strategies has become a key factor in promoting enterprise development. Based on the analysis of customer behavior data, aiming at the redundant features and unbalanced positive and negative samples in customer data, this paper puts forward a new feature selection algorithm and unbalanced data processing algorithm, respectively, to establish a new customer churn prediction model.)