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Rapid development of ultrafast ultraintense laser technologies continues tocreate opportunities for studying strong-field physics under extremeconditions. However, accurate determination of the spatial and temporalcharacteristics of a laser pulse is still a great challenge, especially whenlaser powers higher than hundreds of terawatts are involved. In this paper, byutilizing the radiative spin-flip effect, we find that the spin depolarizationof an electron beam can be employed to diagnose characteristics of ultrafastultraintense 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 afocused Gaussian ultrafast ultraintense laser (in principle, the profile can bearbitrary) with relative errors of $0.1\%$-$10\%$. The underlying physics andan alternative diagnosis method (without the assistance of machine learning)are revealed by the asymptotic approximation of the final spin degree ofpolarization. Our proposed scheme exhibits robustness and detection accuracywith respect to fluctuations in the electron beam parameters. Accuratemeasurements of the ultrafast ultraintense laser parameters will lead to muchhigher precision in, for example, laser nuclear physics investigations andlaboratory astrophysics studies. Robust machine learning techniques may alsofind applications in more general strong-field physics scenarios.

Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin