###### Abstract  
Studying land comprehensive carrying capacity (LCCC) is the foundational and key requirement for determining land development planning and urban spatial development patterns of a region. However, the traditional evaluation method discounts the fuzziness and randomness of the evaluation index and its results. The cloud model combines randomness and fuzziness to reveal the correlation between randomness and fuzziness using numerical feature entropy and is used to represent the granularity of a qualitative concept. This study used the Liaoning Province as the study area, and developed a multi-dimensional evaluation index system for LCCC using a normal cloud model. Based on this, the relationship between the different elements of geological condition, resources and environment, economic scale and urban construction were studied using the coupling coordination degree model that reflected not only the system interactions but also the strengths of its degree of coordination. Our results were as follows: (1) numerical feature entropy were evaluated to determine the carrying capacity level of the land, and comprehensive land carrying capacity evaluations were conducted in terms of both quantitative results and the reliability of the results, promoting the scientific application of uncertainty theory in the field of comprehensive land evaluation as well as carrying capacity. (2) Liaoning Province's prefecture-level cities had distinctly different LCCC, demonstrating \"low in the west and high in the east\" spatial distribution characteristics. Cities with established economies and relatively strong infrastructures had larger comprehensive land carrying capacities. Overall, there was considerable consistency across the region, though the \"low in the west and high in the east\" spatial distribution characteristics affected the degree of coordination. land spatial planning; comprehensive carrying capacity of land; normal cloud model; coupling coordination degree; Liaoning Province +
Footnote †: journal: _Sci._  
0000-0001-5000-00001-50000]X.X. challenges. Particularly, the qualitative conceptualization and quantitative representation of LCCC reveal the intrinsic relevance of randomness and fuzziness, hence making the need for LCCC evaluation universal [3; 4]. Currently, extensive local and global research has been conducted on the total carrying capacity of land [5; 6; 7; 8; 9; 10]. Despite the differences in the current framework, index system and evaluation methods, there are obvious common features, acknowledging that LCCC is a complex system; that the past judgment evaluation of overload are no longer applicable, and that the idea of multi-element, multi-indicator and multi-level evaluation has become a basic consensus; however, the representative methods of LCCC evaluation, such as the agro-ecological zone, ecological footprint, and model index methods, are uncertain, subjective, and have other defects [11; 12; 13; 14; 15]. Enrichment and diversification trends are seen through research findings, which have gradually shifted from the isolated evaluation of a region's total carrying capacity to the combinatorial evaluation of its temporal and spatial characteristics [16; 17], coupling coordination degree analysis [18], index early warning analysis [19], dynamic impact effect, etc. Randomness and fuzziness of indicators are typically not included in research methodologies, which substantially lower the reliability of the findings. The representation and processing of uncertain knowledge are important challenges faced by artificial intelligence, with randomness and ambiguity being the two most fundamental types of uncertainty [20]. However, research on knowledge representation methods has mainly focused on extracting qualitative concepts from quantitative data, and not on developing cognitive models for the bidirectional transformation of qualitative concepts and quantitative data.