Fact verification is intended to check whether a textual statement is supported by a given piece of evidence. Due to the structural dependence and implicit content of tables, the task of fact verification with tables as the evidence still faces many challenges. Existing literature has either used logical expressions to parse statements based on tabular evidence or designed table-aware neural networks to encode statement-table pairs and thereby accomplish table-based fact verification tasks. However, these approaches fail to fully utilize the implicit tabular information behind the statements, which leads to the degraded inference performance of the model. Moreover, Chinese statements based on tabular evidence have more complex syntax and semantics, which also adds to the difficulties in model inference. For this reason, the study proposes a method of fact verification with Chinese tabular data based on the capsule heterogeneous graph attention network (CapsHAN). This method can fully understand the structure and semantics of statements. On this basis, the tabular information implied by the statements is mined and utilized to effectively improve the accuracy of table-based fact verification tasks. Specifically, a heterogeneous graph is constructed by performing syntactic dependency parsing and named entity recognition of statements. Subsequently, the graph is learned and understood by the heterogeneous graph attention network and the capsule graph neural network, and the obtained textual representation of the statements is sliced together with the textual representation of the encoded tables. Finally, the result is predicted. Further, this study also attempts to address the problem that the datasets of fact verification based on Chinese tables are scarce and thus unable to support the performance evaluation of table-based fact verification methods. For this purpose, the study transforms the mainstream English table-based fact verification datasets TABFACT and INFOTABS into Chinese and constructs a dataset that is based on the uniform content label (UCL) national standard and specifically tailored to the characteristics of Chinese tabular data. This dataset, namely, UCLDS, takes Wikipedia infoboxes as evidence of manually annotated natural language statements and labels them into three classes: entailed, contradictory, and neutral. UCLDS outperforms the traditional datasets TABFACT and INFOTABS in supporting both single-table and multi-table inference. The experimental results on the above three Chinese benchmark datasets show that the proposed model outperforms the baseline model invariably, demonstrating its superiority for Chinese table-based fact verification tasks.