reduced the level of well-being of the local people [6]. Under the major goals of the central government and the 17 major goals of the United Nations SDG in 2020, it is of great significance to implement targeted ecological control and anti-poverty work in the rocky desertification areas. In traditional studies, to master the spatial distribution of rocky desertification, researchers must first rely on geological surveys and geographical national surveys to obtain the exposed location of ground rock and the information related to agricultural habitat. Then, based on a combination of field survey sample information and satellite remote sensing data, an indoor mapping operation can be conducted using artificial visual interpretation and multi-spectral and hyperspectral spatial clustering [7; 8; 9]. Finally, the spatial distribution of regional rocky desertification and intensity can be obtained. A large number of studies have been conducted on the rocky desertification formation and developmental evolution mechanisms, the methods for the restoration and reconstruction of degraded ecological environments and the evaluation of their effects [10; 11; 12]. These studies have formed a rich text database that includes research reports, papers, statistical yearbooks and government bulletins. These studies reflect the status and locations of rocky desertification. With the rapid development of Internet search engines and big data technologies, the methods of extracting geographic information from text have been transformed from manual readings to methods that use web crawlers, text mining, machine learning, data modeling to conduct data retrieval, information extraction, geospatial analysis and spatial mapping from online or offline big data and then the results are analyzed in combination with the models and methods in specialized fields [13; 14; 15; 16; 17]. There are many related works about text mining with explicit or implicit geographic messages. In the field of public health detection, [PERSON] and his colleagues detected illness-related messages and predicted real-time infection rates of influenza from geo-tagged Twitter data [18]. For implicit geographic information in text, [PERSON] and his colleagues developed a geolocation system called Carmen to assign a location to each tweet and applied the results to improve influenza surveillance [19]. In the field of nature disaster detection, [PERSON] and colleagues tried to estimate the location of an earthquake event using tweets with locations through the use of spatial models [20]. [PERSON] and his colleagues developed a platform called the ESA-AWTM system to detect, assess, summarize and report messages of interest for crisis coordination published by Twitter and formed a Tweet Map [21]. In addition to social media data, the literature can also be used as a data source. Many tools with similar technical processes of data mining algorithms have been developed to extract gene expression, protein messages or other biological information with locations, such as BioCreative, PubGene, MEDIE and Pubmatrix [22; 23; 24; 25]. In the process of information extraction, there are some mature technological methods for retrieving data and crawling for Internet information [26; 27], such as word segmentation and entity recognition technology [28; 29; 30]. However, it should be noted that most of the past research has focused on specific events, such as epidemics and natural disasters. These events usually explode in the Internet news media in a short period of time. Studies focused on natural and ecological evolution over a long-term scale are relatively rare. In addition, because most research has focused on specific events, the geographic information is usually presented as points after geocoding [31; 32; 33; 34]. However, for natural and ecological evolution, these processes occur in a region rather than a point.