  Particle identification (PID) of hadrons plays a crucial role in particle
physics experiments, especially for flavor physics and jet tagging. The cluster
counting method, which measures the number of primary ionizations in gaseous
detectors, represents a promising breakthrough in PID. However, developing an
effective reconstruction algorithm for cluster counting remains a major
challenge. In this study, we address this challenge by proposing a cluster
counting algorithm based on long short-term memory and dynamic graph
convolutional neural networks for the CEPC drift chamber. Leveraging Monte
Carlo simulated samples, our machine learning-based algorithm surpasses
traditional methods. Specifically, it achieves a remarkable 10\% improvement in
$K/\pi$ separation for PID performance, which meets the necessary PID
requirements for CEPC.
