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The blockchain whitepaper contains detailed technical and business information, so its analysis is important for blockchain text mining. Previous works focus on analyze homogeneous objects and relations. The main problem, however, is these works do not take into account the heterogeneity of information. This paper presents a new methodology for whitepapers analysis by designing heterogeneous graph neural network, named S-HGNN. In detail, this paper first builds a Heterogeneous Information Network (HIN) using heterogeneous objects and relationships extracted from the whitepaper to obtain similarity measures, then uses Graph Convolutional Network (GCN) and Graph Attention Network (GAT) to integrate both structural information and internal semantic into the whitepaper embedding. Compared with the previous models, this model improves 0.96% ∼ 33.34% in terms of F1-score for classification task, and 4.94% ∼ 14.14% in terms of purity for clustering task, and gets stable results on different tasks. The results show the effectiveness and robustness of this model for whitepapers analysis. • We propose a novel automatic blockchain whitepapers analysis framework which integrates both popular data mining and deep learning techniques for intelligent analysis of whitepapers about blockchain products. • This paper explores a heterogeneous Graph Neural Network based on GCN and GAT, and applies the learned feature of whitepapers into clustering and classification tasks. [ABSTRACT FROM AUTHOR]