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In order to reduce the virtual environment data, operation time data at runtime to reflect certain regularity and the classification of, need to intelligent virtual environment big data parallel clustering. The current big data clustering method is the k-means clustering method are based on continuously to adjust the large data samples, after adjusting for multiple computing data parallel clustering effect, but every time a new big data flows, all need to the k-means clustering, are all current data calculation process is complex, low efficiency of the clustering. To this end, this paper proposes a intelligent virtual environment based on graphs of data parallel clustering method. Extracted first big data in a virtual environment of small data set and determine the center of mass of big data clusters, by using the method of Single sampling of small data clustering, average get attributes of the virtual environment is big data, using the minimum distance classification rules will be big data attributes mean to quickly move to real data cluster center, on the basis of Davies - bouldin index assuming a bunch of discrete degree of parameters and data in the parameter values chosen big data intelligent maximum parallel clustering similarity, finally obtained by clustering similarity maximum Davies - bouldin index, on the basis of Davies - bouldin index to multiple categories of discrete degree of specified threshold centroid distance and clustering combined into a class and the iterative calculation, get the best data clustering center position, resulting in a virtual environment intelligent parallel clustering large data. The simulation experimental results show that the proposed method improves the big data intelligent parallel clustering flexibility and generality, reduce the time of clustering, and suitable for application in the field of education technology, not only can make education technology network data more rationalization, and more standardized.