With the rapid development of Internet technology, Web has become a huge information source with massive amounts of data. But these data are usually embedded in the semi-structured pages. In order to use these data effectively, the primary problem is to extract the data and store them in structured form. Most of current approaches use a single classifier to extract web data, but relying on a single classifier is not sufficient and different classifier has different performance for the same problem. In this paper, we use the method of ensemble learning for web data extraction. Firstly, we parse the page as a Dom tree, identify the main data regions, and construct feature sets of text nodes in the region. Secondly, we choose multiple kinds of base classifiers (SVM, KNN and Random Forest) to build classification models and then use the linear method to integrate results of each classification model. Finally, we combine integration results with heuristic rules to get the final extraction results. The experiment results show that our approach outperforms the baseline approaches and has a good robustness.