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Concept lattice is an effective tool for data analysis and rule acquisition. In recent years, the application and research of concept lattice has gradually become an important research direction in the field of data analysis. With the development of information technology, stream data have become an important part of big data, and the concept drift in stream data mining has become a hot topic in machine learning. The construction of dynamic concept lattice is an important research task of concept lattice theory, but the research of concept lattice drift in streaming data environment has not been carried out. To solve the problem of concept lattice drift in stream data environment, the drift calculation method of concept lattice based on sliding window method is proposed in this paper. First, the stream data in the sliding window are modeled. Then, in the sliding window, this paper conducts inference research separately for five phenomena, i.e. the same inflow and outflow concepts, different inflow and outflow concepts, partial intersection of inflow and outflow concepts, inflow concept including outflow concept, and the outflow concept including the inflow concept. Finally, based on the above model reasoning, a concept lattice construction algorithm based on sliding window method is proposed, and an example is given to illustrate the effectiveness and efficiency of the algorithm.