For the purpose of detecting gravitational waves from space, high-energy particles will infiltrate the satellite’s structure and accumulate charges on the test mass, consequently diminishing detection performance. Present investigations into the charging rate of the test mass typically rely on the particle distribution of Earth orbit. However, the influence of the Earth’s magnetosphere needs to be considered for the Tianqin detector because of its geocentric orbit. In this paper, artificial neural network is used to fuse satellite data and the galactic cosmic rays model to give the distribution of high-energy particles in the Tianqin orbit. And then, the charging rate is estimated by Monte Carlo simulation. The simulation results show that the flux of hundreds of MeV particles is a little higher than that in Earth orbit, which lead to 3−4 e s−1 higher than LISA, i.e. the charging rate of approximately 43 e s−1 during periods of solar minimum and 19 e s−1 during periods of solar maximum. These findings hold substantial implications for guiding the design of the charge management system.