Broad learning systems(BLS) have been widely used in many fields because of its strong feature extraction ability and high computational efficiency. However, the BLS is mainly used for single-output regression at present, When the BLS has multi-outputs, it cannot effectively explore the correlation between multi-output weights, which will lead to the degradation of model prediction performance. Therefore, by introducing the Frobenius and $L_{2,1