  We present a novel approach to deriving stellar labels for stars observed in
MUSE fields making use of data-driven machine learning methods. Taking
advantage of the comparable spectral properties (resolution, wavelength
coverage) of the LAMOST and MUSE instruments, we adopt the Data-Driven Payne
(DD-Payne) model used on LAMOST observations and apply it to stars observed in
MUSE fields. Remarkably, in spite of instrumental differences, according to the
cross-validation of 27 LAMOST-MUSE common stars, we are able to determine
stellar labels with precision better than 75K in $T_{\rm eff}$, 0.15 dex in
$\log g$, and 0.1 dex in abundances of [Fe/H], [Mg/Fe], [Si/Fe], [Ti/Fe],
[C/Fe], [Ni/Fe] and [Cr/Fe] for current MUSE observations over a parameter
range of 3800<$T_{\rm eff}$<7000 K, -1.5<[Fe/H]<0.5 dex. To date, MUSE has been
used to target 13,000 fields across the southern sky since it was first
commissioned six years ago and it is unique in its ability to study dense star
fields such as globular clusters or the Milky Way bulge. Our method will enable
the automated determination of stellar parameters for all stars in these
fields. Additionally, it opens the door for applications to data collected by
other spectrographs having resolution similar to LAMOST. With the upcoming
BlueMUSE and MAVIS, we will gain access to a whole new range of chemical
abundances with higher precision, especially critical s-process elements such
as [Y/Fe] and [Ba/Fe] that provide key age diagnostics for stellar targets.
