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(School of Automation and Information Engineering, Xi"an University of Technology, Xi"an, 710048)：With the rapid growth of Internet data, the original Kmeans algorithm is no longer sufficient to meet the clustering needs of large-scale data. To this end, an improved Canopy-Kmeans clustering algorithm is proposed. Faced with the shortcomings of the random selection of the center point of the Canopy algorithm, the "maximum principle" was introduced to optimize the selection of the Canopy center point; then the Kmeans algorithm was optimized with the help of the triangle inequality theorem to reduce redundant distance calculations and accelerate the convergence rate of the algorithm; finally Combined with MapReduce framework parallelization to achieve improved Canopy-Kmeans algorithm. Based on the constructed Weibo dataset, the optimized Canopy-Kmeans algorithm is tested. The test results show that the accuracy of the optimized algorithm is about 15% higher than that of the Kmeans algorithm and about 7% higher than that of the original Canopy-Kmeans algorithm. The execution efficiency and scalability of the algorithm are also improved. Greatly improved.