  A deep learning method for the particle trajectory reconstruction with the
DAMPE experiment is presented. The developed algorithms constitute the first
fully machine-learned track reconstruction pipeline for space astroparticle
missions. Significant performance improvements over the standard
hand-engineered algorithms are demonstrated. Thanks to the better accuracy, the
developed algorithms facilitate the identification of the particle absolute
charge with the tracker in the entire energy range, opening a door to the
measurements of cosmic-ray proton and helium spectra at extreme energies,
towards the PeV scale, hardly achievable with the standard track reconstruction
methods. In addition, the developed approach demonstrates an unprecedented
accuracy in the particle direction reconstruction with the calorimeter at high
deposited energies, above several hundred GeV for hadronic showers and above a
few tens of GeV for electromagnetic showers.
