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A continuing challenge for Integration Information of Table Tennis Training (TTT) is to develop efficient data unit component part. Many see data unit integration as a potential solution; however, the quality of data integration of Table Tennis Training tends to outweigh the potential benefits. The quality of TTT data unit integration include establishing and maintaining a data warehouse of Table Tennis Training data unit, searching for applicable data unit to be integrated in a design, as well as adapting data unit toward a proper structure. In this paper, a new Data Feature Model (DFM) method is suggested here for data classification and integration of TTT which consists of K-Shortest Path (KSP) algorithm and Data Feature Model Method.
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