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With the popularity of GPU hardware and the rapid development of GPGPU technology, more researchers have invested in GPGPU research. Beacuse of the strong parallel computing power, floating-point computing power, computing unit integration capabilities and other characteristics, GPU shows the great potential in the field of parallel computing. CUDA is an architecture developed by NVIDIA for parallel computing using a GPU and it makes the GPU highly user-friendly and provides a convenient way for researchers to implement scientific computing in a variety of fields on the GPU. K-means clustering algorithm has become a popular direction for parallel computing because of its simple concept and easy realization. For the K-means parallel algorithm, there is a 8-core CPU and FPGA-based accelerator board method, but for a complex model that needs to start thousands of threads and the traditional CPU parallel computing method is difficult to achieve; What’s more, some researches have study the K-means clustering algorithm based on the CUDA parallel computing platform, but the studies usually ignore the algorithm optimization. Based on the above shortcomings, the K-means clustering algorithm is introduced on the CUDA platform, and the K-means clustering parallel computation based on sliding gate center point calculation is proposed. The The experimental results show that when the number of clusters is large, the parallel algorithm based on the sliding gate is more efficient than the traditional updating center algorithm.