With the increasingly urgent demand of map conflation and timely data updating, data matching becomes a crucial issue in big data and GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale difference occur to crowdsourcing and official building data, causing great challenges to conflate heterogeneous building dataset from difference sources and scales. The paper thus proposes an automated building data matching method based on relaxation labelling and pattern combinations. The proposed method first detects all possible matching objects and pattern combinations to create a matching table, and calculates four geo-similarities for each candidate-matching pair to initialize a probabilistic matching matrix. After that, the contextual information of neighboring candidate-matching pairs are explored to heuristically amend the geo-similarity-based matching matrix for achieving a contextual matching consistency. Three case studies are conducted to illustrate that the proposed method obtains high matching accuracies and correctly identifies various 1: 1, 1: M and M: N matching. That indicates the pattern-level relaxation labelling matching method can efficiently overcome the problems of shape homogeneity and non-rigid deviation, and meanwhile has weak sensitivity to uncertain scale differences, providing a functional solution for conflating crowdsourcing and official building data.