The　broad　learning　system　(BLS)　is　an　emerging　flat　network,　which　has　demonstrated　its　outstanding　performance　in　classification　and　regression　problems.　The　regularization　plays　an　important　role　in　the　performance　of　the　BLS.　In　real　applications,　since　the　BLS　network　is　usually　expanded　dynamically,　a　predetermined　regularization　parameter　may　reduce　the　performance　of　the　network.　Using　a　fixed　regularization　in　some　cases,　the　classification　accuracy　of　the　BLS　decreases　dramatically　when　we　expand　the　network.　To　alleviate　this　problem,　we　propose　a　method　that　automatically　finds　appropriate　regularization　parameters　for　different　datasets,　which　is　based　on　the　weighted　generalized　cross-validation　(WGCV).　The　experimental　results　indicate　that　the　WGCV　method　improves　the　performance　of　the　BLS,　and　alleviates　the　accuracy　decrease　of　the　incremental　learning　algorithm.