  Accurately measuring the Hubble parameter is vital for understanding the
expansion history and properties of the universe. In this paper, we propose a
new method that supplements the covariance between redshift pairs to improve
the reconstruction of the Hubble parameter using the OHD dataset. Our approach
utilizes a cosmological model-independent radial basis function neural network
(RBFNN) to describe the Hubble parameter as a function of redshift effectively.
Our experiments show that this method results in a reconstructed Hubble
parameter of $H_0 = 67.1\pm9.7~\mathrm{km~s^{-1}~Mpc^{-1}}$ , which is more
noise-resistant and fits better with the $\Lambda$CDM model at high redshifts.
Providing the covariance between redshift pairs in subsequent observations will
significantly improve the reliability and accuracy of Hubble parametric data
reconstruction. Future applications of this method could help overcome the
limitations of previous methods and lead to new advances in our understanding
of the universe.
