  The discovery of habitable exoplanets has long been a heated topic in
astronomy. Traditional methods for exoplanet identification include the wobble
method, direct imaging, gravitational microlensing, etc., which not only
require a considerable investment of manpower, time, and money, but also are
limited by the performance of astronomical telescopes. In this study, we
proposed the idea of using machine learning methods to identify exoplanets. We
used the Kepler dataset collected by NASA from the Kepler Space Observatory to
conduct supervised learning, which predicts the existence of exoplanet
candidates as a three-categorical classification task, using decision tree,
random forest, na\"ive Bayes, and neural network; we used another NASA dataset
consisted of the confirmed exoplanets data to conduct unsupervised learning,
which divides the confirmed exoplanets into different clusters, using k-means
clustering. As a result, our models achieved accuracies of 99.06%, 92.11%,
88.50%, and 99.79%, respectively, in the supervised learning task and
successfully obtained reasonable clusters in the unsupervised learning task.
