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(abridged) We develop a tool for the automated spectral classification of OBstars according to their sub-types. We use the regular Random Forest (RF)algorithm, the Probabilistic RF (PRF), and we introduce the KDE-RF method whichis a combination of the Kernel-Density Estimation and the RF algorithm. Wetrain the algorithms on the Equivalent Width (EW) of characteristic absorptionlines (features) measured in high-quality spectra from large Galactic(LAMOST,GOSSS) and extragalactic surveys (2dF,VFTS) with availablespectral-types and luminosity classes. We find that the overall accuracy scoreis $\sim$70% with similar results across all approaches. We show that the fullset of 17 spectral lines is needed to reach the maximum performance perspectral class. We apply our model in other observational data sets providingexamples of potential application of our classifier on real science cases. Wefind that it performs well for both single massive stars and for the companionmassive stars in Be X-ray binaries. In addition, we propose a reduced10-features scheme that can be applied to large data sets with lower S/N. Thesimilarity in the performances of our models indicates the robustness and thereliability of the RF algorithm when it is used for the spectral classificationof early-type stars. The score of $\sim$70% is high if we consider (a) thecomplexity of such multi-class classification problems, (b) the intrinsicscatter of the EW distributions within the examined spectral classes, and (c)the diversity of the training set since we use data obtained from differentsurveys with different observing strategies. In addition, the approachpresented in this work, is applicable to data of different quality and ofdifferent format (e.g.,absolute or normalized flux) while our classifier isagnostic to the Luminosity Class of a star and, as much as possible,metallicity independent.

A new automated tool for the spectral classification of OB stars