Abstract:

This paper constructed an evaluation system based on search engine index， tourism website index， social media index and short video index， by using spatial classification， kernel density estimation， nearest neighbor analysis and other methods to analyze the spatial difference of network attention in A-level desert scenic spots in China， and then explored the influencing factors. The results were shown as follows. （1） Desert scenic spots with high attention were mainly concentrated in Inner Mongolia， Xinjiang and Gansu， and the scenic spots with low attention had a large number， wide distribution， small scale and low level. From the perspective of subsystem， the spatial distribution pattern of search engine， tourism website and social media was similar to the comprehensive level， while the short video subsystem was quite different from the comprehensive level. （2） In the spatial agglomeration pattern， there was a pattern of one main core， one sub core， multi-core linkage. From the perspective of subsystem， the three subsystems of search engine， tourism website and social media were similar to the comprehensive level， and the short video subsystem was quite different from other subsystems. （3） In the spatial structure characteristics， the overall distribution state of the spots of medium and higher level tend to be uniform， the spots of lower level tend to be random， and the spots of low level tend to be condensed， and the agglomeration characteristics gradually increased with the decrease of the attention level of the scenic spots. Search engines and tourism websites were similar to the comprehensive level. （4） From the perspective of influencing factors， the factors that have a significant impact on the network attention of desert scenic spots were the sales market， the construction of propaganda platform and the regional basic strength， followed by the basic strength of scenic spots， and the influence of external tourism environment was relatively weak.

Key words: network attention, desert tourism, A-level scenic spots, spatial pattern, China