The rapid development of technology and increasing numbers of customers have saturated the communication market. Communication operators must give focused attention to the problem of customer churn. Analyzing the customer’s communication behavior and building a prediction model of customer churn can provide the advance evidence for communication operators to minimize churn. This paper describes how to design a HMM to predict customer churn based on communication data. First, we oversample churners to increase the number of positive samples and establish the relative balance of positive and negative samples. Second, the continuous numerical attributes that affect communication customer churn are relatively discretized and their monthly values are converted into monthly change tendencies. Next, we select the communication features by calculating the information gains and information gain rates of all communication attributes. We then construct and optimize a prediction model of customer churn based on HMM. Finally, we test and evaluate the model by using a Spark cluster and the communication data set of Taizhou Branch of China Telecom. Experimental evaluation provides proof that our prediction model is exceptionally reliable.