To address the computationally inefficient problem of the classical support vector regression model when processing the large-scale data, this paper combines the communication-efficient method with the support vector regression model to propose a distributed support vector regression model (CE-SVR). CE-SVR model firstly uses distributed storage to randomly distribute the large-scale data into multiple machines, then uses a communication-efficient method to construct an approximate loss function of support vector regression instead of the global loss function and obtain an approximate prediction result, which can effectively solve the limitations of the classical support vector machine regression model. The results of the numerical simulation and applied research show: In the linear model, the prediction performance of the CE-SVR model is basically consistent with the global support vector regression model, and is significantly better than the distributed support vector regression model (OS-SVR) based on one-shot method; in the nonlinear model, the prediction performance of CE-SVR model decreases as the number of machines increasing, but its prediction performance is significantly better than that of the OS-SVR model.