In the era of astronomical big data, more than one million contact binaries have been discovered. Traditional approaches of light-curve analysis are inadequate for investigating such an extensive number of systems. This paper builds on prior research to present an advanced neural network model combined with the Markov Chain Monte Carlo algorithm and including spot parameters. This model was applied to 12,785 contact binaries selected from All-Sky Automated Survey for Supernovae. By removing those with a goodness of fit less than 0.8, we obtained the physical parameters of 12,201 contact binaries. Among these binaries, 4332 are A-subtype systems, while 7869 are W-type systems, and 1594 systems have mass ratios larger than 0.72 (H-subtype systems). A statistical study of the physical parameters was carried out, and we found that there are two peaks in the mass-ratio distribution and that the probability of the presence of spots is about 50%. In addition, the differences in flux between the two light maxima are from -0.1 to 0.1. As the orbital period and the temperature of the primary component decrease, the difference between the two light maxima becomes more pronounced. Based on the relationships between the transfer parameter and luminosity ratio, as well as between the luminosity ratio and mass ratio, we found that A-, W-, and H-type contact binaries are distributed in distinct regions.