  Identifying and classifying quantum phases from measurable time series in
many-body dynamics have significant values, yet face formidable challenges,
requiring profound knowledge of physicists. Here, to achieve a pure data-driven
machine intelligent classification, we introduce a complexity boosted distance
measure that captures the inherent complexity of dynamic evolution series in
different quantum many-body phases. Significantly, the introduction of
complexity-boosted distance leads to remarkable improvements of unsupervised
manifold learning of quantum many-body dynamics, which are exemplified in
discrete time crystal model, Aubry-Andr\'e model, and quantum east model. Our
method does not require any prior knowledge and exhibits effectiveness even in
imperfect, disordered, and noisy situations that are challenging for human
scientists. Successful classification of dynamic phases in many-body systems
holds the potential to enable crucial applications, including identification of
tsunamis, earthquakes, catastrophes and future trends in finance.
