The accurate prediction of residue-residue contacts provides crucial help to the ab initio protein folding and 3D structure modeling, because the accurately predicted contacts can enforce useful constraints to the structure assembly. Recent CASP experiments have witnessed the prosperities on this topic and a number of promising protein contact map predictors have emerged in the past decades. Although much progress has been made, challenges (e.g., low prediction accuracy for long-range contacts) remain. Here we developed a new meta-based predictor, called TargetPCM, which can achieve high accuracy for protein contact map prediction. TargetPCM combines the outputs of three existing powerful contact map predictors by using a weighted Naïve Bayes classifier (WNBC), among which the weight parameters are optimized with particle swarm optimization (PSO) algorithm. Then, the outputs of WNBC are further combined with the intrinsic sequence-based features and fed to the final prediction model, which is trained with extremely randomized trees (ERT), for performing contact map prediction. Tested on 98 non-redundant proteins, our TargetPCM improves the Top L/5 accuracy over the best meta-based predictor (NeBcon) by 8.2%, 16.1% and 5.3%, respectively, for short-, medium- and long-range contacts. Further investigations on CASP 11 show that TargetPCM improves the Top L/5 accuracy over the best co-evolution based meta-server predictor (MetaPSICOV) by 7.4%, 9.1% and 7.5%, respectively, for short-, medium- and long-range contacts. Detailed analysis on the experimental results shows that both the effective utilization of complementary information from base predictors and the powerful learning capability of ERT account for the performance improvements of the proposed TargetPCM over existing contact map predictors.