Following the recent advances in deep learning techniques, in this paper, we present the application of special type of deep architecture — deep convex networks (DCNs) — for semantic utterance classification (SUC). DCNs are shown to have several advantages over deep belief networks (DBNs) including classification accuracy and training scalability. However, adoption of DCNs for SUC comes with non-trivial issues. Specifically, SUC has an extremely sparse input feature space encompassing a very large number of lexical and semantic features. This is about a few thousand times larger than the feature space for acoustic modeling, yet with a much smaller number of training samples. Experimental results we obtained on a domain classification task for spoken language understanding demonstrate the effectiveness of DCNs. The DCN-based method produces higher SUC accuracy than the Boosting-based discriminative classifier with word trigrams.