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Systems related to knowledge management can improve quality and efficiency of knowledge used for decision making process. Approximately 80 percent of corporate information are in textual data formats. That is why text mining is useful and important in service chain knowledge management. For example, one of the most important applications of text mining is in managing on-line source of digital documents and the analysis of internal documents. This research is based on text-based documents and textual information and interviews processed by Grounded theory. In this research clustering techniques were applied at first step. In the second step, Apriori association rules techniques for discovering and extracting the most useful association rules were applied. In other words, integration of datamining techniques was emphasized to improve the accuracy and precision of classification. Using decision tree technique for classification may result in reducing classification precision. But, the proposed method showed a significant improvement in classification precision.