Higherorder multilabel learning algorithm can mine the relationships or subsets of all class labels， however， it has some drawbacks in dealing with the problems of error propagation and redundant or erroneous class dependencies. For such problems， a new multilabel classifier SCC， i.e.， Sparse Classifier Chains， was proposed based on classier chains by introducing sparse learning. In order to verify the effectiveness of the proposed algorithm， we compared this with other 5 kinds of multilabel learning algorithms and selected 8 evaluation indexes to evaluate the performance of them. The experimental verification which was carried out on 12 standard data set lastly analyzed by using the rank sum test method to compare the relative performance . The experimental results showed that SCC was superior to all the contrast algorithms， significantly superior to some algorithms； furthermore， it had a strong generalization performance.