For curve skeleton extraction algorithm, in order to improve the efficiency of potential field computation and save the time of extraction process, we presented a parallel potential field skeleton extraction method to reduce the time complexity, which was suitable for implementation on GPU, and then improved it by using constant memory and shared memory which was unique in CUDA. In order to achieve the highest GPU occupancy and the best speedups, we discussed how to assign threads according to the property of program and graphics device. The implementation was tested on several complex 3D models in CUDA framework. The results showed that our method had excellent performance especially on large data scale. When processing the volume data with the scale of 256×256×487, this improved method achieved speedups of 18x.%为解决势能场骨架提取方法计算效率低、提取过程耗时大的问题，同时为降低该方法的时间复杂度，提出了基于GPU的势能场骨架提取并行算法，并充分利用CUDA架构特有的常量存储器和共享存储器对普通并行算法进行改进。讨论了如何根据程序和显卡设备的固有属性来分配线程以达到最高的GPU占用率，从而得到最优的加速效果。对多组3D模型进行测试的结果表明，随着数据规模的增大，加速效果逐渐提升，处理256×256×487的体数据时，可获得18倍的加速比。
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