Abstract: In order to fully clarify the spatial network structure characteristics of carbon emissions in China's tourism industry, based on the data of carbon emissions (excluding the data of Tibet Autonomous Region, Hongkong, Macao and Taiwan, the rest is the same) from the tourism industry of various provinces, autonomous regions and municipalities directly under the central government in China from 2000 to 2015, and by combining them with the improved gravity model, this paper constructs the spatial network correlation of carbon emissions in tourism industry. Social network analysis method is applied to the analysis of the spatial correlation of carbon emissions in tourism industry and its influencing factors in China. The results show that:(1) During the investigation period, the spatial network correlation degree of carbon emissions in the tourism industry is always 1, the number of its network relations and the network density continue to rise, while the network hierarchy and efficiency steadily decline. (2) The eastern developed regions such as Shanghai City, Zhejiang Province, Jiangsu Province, Beijing City, Tianjin City, etc. are at the core position of the network, which have a significant impact on the spatial correlation of carbon emissions in tourism industry. Relatively backward regions such as Hainan Province, Yunnan Province, Guangxi Zhuang Autonomous Region, Qinghai Province, Jilin Province, etc. are at the edge position of the network, and have little effect on the spatial correlation of carbon emissions in the tourism industry. (3) Regions such as Guangxi Zhuang Autonomous Region, Guizhou Province and Xinjiang Uygur Autonomous Region are classified as 'net spillover plate'; Hebei Province, Gansu Province, Shaanxi Province and so on are in the 'agent plate'; Beijing City, Tianjin City and Inner Mongolia Autonomous Region are classified as 'bidirectional spillover plate'; Jiangsu Province, Zhejiang Province as well as Shanghai City belong to the 'net benefit plate'. (4) The spatial adjacency relation and urbanization level disparity have a positive effect on the spatial correlation of tourism carbon emissions at a significant level of 1%; The differences of tourism consumption level and industrial structure are positively correlated with the spatial correlation of tourism carbon emissions at the significant level of 5% and 10%; The energy consumption disparity has a negative correlation with the spatial correlation of tourism carbon emissions at the significant level of 1%. The research shows that the spatial correlation of carbon emissions in tourism industry is closely related, yet there is still much space for improvement. The spatial correlation between the plates needs to be further strengthened.