An effective polarimetric synthetic aperture radar (PolSAR) image classification technology is the basis of the successful application of PolSAR. However, compared with relatively mature PolSAR imaging technology and system design, PolSAR image classification technology lags behind. Aiming at the main problems existing in the research of object-oriented classification of PolSAR images, this paper proposed a new object-oriented classification method, which combines multi-target polarimetric decomposition, ReliefF-PSO_SVM and ensemble learning. First, polarimetric decomposition is implemented for PolSAR image using various methods. Polarimetric parameters extracted using different polarimetric decomposition methods are combined into a multichannel image. Second, the multichannel image is divided into numerous image objects by implementing multi-resolution segmentation. Third, features are extracted from the multichannel image. Fourth, ReliefF-PSO_SVM algorithm is applied for feature selection, and N feature subsets with the highest fitness are retained for classification. Each feature subset corresponds to a classification result. Finally, ensemble learning technology is used to integrate the classification results. The study site is located at the southeastern part of Changchun City, Jilin Province. A RADARSAT-2 Fine Quad-Pol image was selected as the data source for this study. The proposed method was applied to land-use classification, and good classification results were obtained. The overall accuracy was 85.06% and the kappa value was 0.8006. In addition, three other classification methods were performed for comparison. The comparison results further proved the superiority of the proposed method in PolSAR image classification.