Semistructured data are specified in lack of any fixed and rigid schema, even though typically some implicit structure appears in the data. The huge amounts of on-line applications make it important and imperative to mine the schema of semistructured data, both for the users (e.g., to gather useful information and facilitate querying) and for the systems (e.g., to optimize access). The critical problem is to discover the hidden structure in the semistructured data. Current methods in extracting Web data structure are either in a general way independent of application background, or bound in some concrete environment such as HTML, XML etc. But both face the burden of expensive cost and difficulty in keeping along with the frequent and complicated variances of Web data. In this paper, the problem of incremental mining of schema for semistructured data after the update of the raw data is discussed. An algorithm for incrementally mining the schema of semistructured data is provided, and some experimental results are also given, which show that incremental mining for semistructured data is more efficient than non-incremental mining.