An efficient SVM-based hotspot detection method using spectral clustering is proposed in this paper. Firstly, we build graphs to represent both training patterns and test layouts. With spectral clustering, the training patterns and test layouts are adaptively decomposed into a set of small critical patterns. The small critical patterns from the training data sets are used to build the SVM models. The SVM models are then employed to check the decomposed features from the test layouts for hotspot detection. Compared with existing SVM-based hotspot detection methods, the proposed method can achieve higher accuracy with comparable number of false alarms and significant speedup.