Abstract: It is an inherently challenging scientific endeavor to understand and predict multi-scale, high-dimensional and nonlinear seismological phenomena. The increasing amount of observational big data breaks the linkage between data collection and interpretation, and increases the obscurity and uncertainty in data analysis. However, there is also artificial intelligent computer technology, i.e. machine learning in the era of big data. The excellent capability of machine learning for implicit relation extraction and complex task processing has enabled it to be applied to a variety of fields. In this article, we introduce some of the commonly used machine learning algorithms in seismology as well as their applications, and discuss the future directions of integrating artificial intelligence with seismic data interpretation.