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The wrapper network fault feature selection algorithms get large calculation cost, a Meta-learning and binary particle swarm optimization (ML-BPSO) based feature selection algorithm was proposed to solve this problem in this paper. The Meta-learning method was introduced for estimating the classification accuracy wrapped in selected method. On this basis, the BPSO is used for searching the whole feature space to find the best feature subset. The experiment on DARPA datasets shows the proposed method result approximate to BPSO-SVM and the calculation cost reduced expressly. The result shows ML-BPSO reduce the calculation cost while gets good performance on classification accuracy and dimensional decrease.