  Exoplanet detection in the past decade by efforts including NASA's Kepler and
TESS missions has discovered many worlds that differ substantially from planets
in our own Solar System, including more than 150 exoplanets orbiting binary or
multi-star systems. This not only broadens our understanding of the diversity
of exoplanets, but also promotes our study of exoplanets in the complex binary
systems and provides motivation to explore their habitability. In this study,
we investigate the Habitable Zones of circumbinary planets based on planetary
trajectory and dynamically informed habitable zones. Our results indicate that
the mass ratio and orbital eccentricity of binary stars are important factors
affecting the orbital stability and habitability of planetary systems.
Moreover, planetary trajectory and dynamically informed habitable zones divide
planetary habitability into three categories: habitable, part-habitable and
uninhabitable. Therefore, we train a machine learning model to quickly and
efficiently classify these planetary systems.
