Directed Self-Assembly (DSA) is a promising technique for contacts/vias patterning, where groups of contacts/vias are patterned by guiding templates. As the templates are pat- terned by traditional lithography, their shapes may vary due to the process variations, which will ultimately affect the con- tacts/vias even for the same type of template. Due to the complexity of the DSA process, rigorous process simulation is unacceptably slow for full chip verication. This paper formulate several critical problems in DSA verication, and proposes a design automation methodology that consists of a data preparation and a model learning stage. We present a novel DSA model with Point Correspondence and Segment Distance features for robust learning. Following the method- ology, we propose an effective machine learning (ML) based method for DSA hotspot detection. The results of our initial experiments have already demonstrated the high-efficiency of our ML-based approach with over 85% detection accuracy. Compared to the minutes or even hours of simulation time in rigorous method, the methodology in this paper validates the research potential along this direction.