  Rapid development of ultrafast ultraintense laser technologies continues to
create opportunities for studying strong-field physics under extreme
conditions. However, accurate determination of the spatial and temporal
characteristics of a laser pulse is still a great challenge, especially when
laser powers higher than hundreds of terawatts are involved. In this paper, by
utilizing the radiative spin-flip effect, we find that the spin depolarization
of an electron beam can be employed to diagnose characteristics of ultrafast
ultraintense lasers with peak intensities around $10^{20}$-$10^{22}$~W/cm$^2$.
With three shots, our machine-learning-assisted model can predict,
simultaneously, the pulse duration, peak intensity, and focal radius of a
focused Gaussian ultrafast ultraintense laser (in principle, the profile can be
arbitrary) with relative errors of $0.1\%$-$10\%$. The underlying physics and
an alternative diagnosis method (without the assistance of machine learning)
are revealed by the asymptotic approximation of the final spin degree of
polarization. Our proposed scheme exhibits robustness and detection accuracy
with respect to fluctuations in the electron beam parameters. Accurate
measurements of the ultrafast ultraintense laser parameters will lead to much
higher precision in, for example, laser nuclear physics investigations and
laboratory astrophysics studies. Robust machine learning techniques may also
find applications in more general strong-field physics scenarios.
