  We investigate theoretically the phase transition in three dimensional cubic
Ising model utilizing state-of-the-art machine learning algorithms. Supervised
machine learning models show high accuracies (~99\%) in phase classification
and very small relative errors ($< 10^{-4}$) of the energies in different spin
configurations. Unsupervised machine learning models are introduced to study
the spin configuration reconstructions and reductions, and the phases of
reconstructed spin configurations can be accurately classified by a linear
logistic algorithm. Based on the comparison between various machine learning
models, we develop a few-shot strategy to predict phase transitions in larger
lattices from trained sample in smaller lattices. The few-shot machine learning
strategy for three dimensional(3D) Ising model enable us to study 3D ising
model efficiently and provides a new integrated and highly accurate approach to
other spin models.
