The order of label learning is crucial to a classifier chains method. Therefore, this study proposes a classifier chains method based on the association rules and topological sequence (TSECC). Specifically, a measurement strategy for label dependencies based on strong association rules is designed by leveraging frequent patterns. Then, a directed acyclic graph is constructed according to the dependency relationships among the labels to topologically sort all the vertices in the graph. Finally, the topological sequence obtained is used as the order of label learning to iteratively update each label’s classifier successively. In particular, to reduce the impact of “lonely” labels with no or low label dependencies on the prediction performance on the other labels, TSECC excludes “lonely” labels out of the topological sequence and uses a binary relevance model to train them separately. Experimental results on a variety of public multi-label datasets show that TSECC can effectively improve classification performance.