Abstract: The most existing frameworks of novel class detection have low novel class detection accuracy and slow processing rate for dividing the data stream into fixed-size chunks, and it is not realistic that all the attributes of data objects have the same weight in the existing framework, a classification algorithm for novel class detection based on data stream with concept-drift(DNCS) is proposed. The algorithm periodically detects the data distribution in the sliding window, dynamically changes the size of the chunk and updates the model to adapt to the novel data. The improved algorithm makes the clustering algorithm based on attribute weight the basic step for detecting novel class. The experimental results show that DNCS has higher novel class detection accuracy and processing speed.