  We use the machine learning technique to search the polytope which can result
in an orientifold Calabi-Yau hypersurface and the "naive Type IIB string
vacua". We show that neural networks can be trained to give a high accuracy for
classifying the orientifold property and vacua based on the newly generated
orientifold Calabi-Yau database with $h^{1,1}(X) \leq 6$ arXiv:2111.03078. This
indicates the orientifold symmetry may already be encoded in the polytope
structure. In the end, we try to use the trained neural networks model to go
beyond the database and predict the orientifold signal of polytope for higher
$h^{1,1}(X)$.
