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Bulge-disk (B-D) decomposition is an effective diagnostic to characterize thegalaxy morphology and understand its evolution across time. So far,high-quality data have allowed detailed B-D decomposition to redshift below0.5, with limited excursions over small volumes at higher redshifts.Next-generation large sky space surveys in optical, e.g. from the China SpaceStation Telescope (CSST), and near-infrared, e.g. from the space EUCLIDmission, will produce a gigantic leap in these studies as they will providedeep, high-quality photometric images over more than 15000 deg2 of the sky,including billions of galaxies. Here, we extend the use of the Galaxy Lightprofile neural Network (GaLNet) to predict 2-S\'ersic model parameters,specifically from CSST data. We simulate point-spread function (PSF) convolvedgalaxies, with realistic B-D parameter distributions, on CSST mock observationsto train the new GaLNet and predict the structural parameters (e.g. magnitude,effective radius, Sersic index, axis ratio, etc.) of both bulge and diskcomponents. We find that the GaLNet can achieve very good accuracy for most ofthe B-D parameters down to an $r$-band magnitude of 23.5 and redshift $\sim$1.The best accuracy is obtained for magnitudes, implying accurate bulge-to-total(B/T) estimates. To further forecast the CSST performances, we also discuss theresults of the 1-S\'ersic GaLNet and show that CSST half-depth data will allowus to derive accurate 1-component models up to $r\sim$24 and redshiftz$\sim$1.7.

Galaxy Light profile neural Networks (GaLNets). II. Bulge-Disc decomposition in optical space-based observations