  The impact parameter is one of the crucial physical quantities of heavy-ion
collisions (HICs), and can affect obviously many observables at the final
state, such as the multifragmentation and the collective flow. Usually, it
cannot be measured directly in experiments but might be inferred from
observables at the final state. Artificial intelligence has had great success
in learning complex representations of data, which enables novel modeling and
data processing approaches in physical sciences. In this article, we employ two
of commonly used algorithms in the field of artificial intelligence, the
Convolutional Neural Networks (CNN) and Light Gradient Boosting Machine
(LightGBM), to improve the accuracy of determining impact parameter by
analyzing the proton spectra in transverse momentum and rapidity on the
event-by-event basis. Au+Au collisions with the impact parameter of
0$\leq$$b$$\leq$10 fm at intermediate energies ($E_{\rm lab}$=$0.2$-$1.0$
GeV$/$nucleon) are simulated with the ultrarelativistic quantum molecular
dynamics (UrQMD) model to generate the proton spectra data. It is found that
the average difference between the true impact parameter and the estimated one
can be smaller than 0.1 fm. The LightGBM algorithm shows an improved
performance with respect to the CNN on the task in this work. By using the
LightGBM's visualization algorithm, one can obtain the important feature map of
the distribution of transverse momentum and rapidity, which may be helpful in
inferring the impact parameter or centrality in heavy-ion experiments.
