As the foundational and core resource for agricultural development, arable land plays a critical role in optimizing regional agricultural resource allocation and ensuring food security. This study employs Morphological Spatial Pattern Analysis (MSPA) and the Landscape Pattern Index (LPI) to comprehensively analyze the erosion and fragmentation of arable land in Shandong Province from 1990 to 2023. Bivariate spatial autocorrelation (BSA), Pearson correlation coefficients and the Self-Organizing Map (SOM) algorithm are applied to examine the relationships among landscape pattern indices and spatial clustering patterns. Finally, the driving factors are explored using Multiscale Geographically Weighted Regression (MGWR). The results demonstrate the following: (1) The results indicate a rapid decline in core cultivated zones across central and southeastern coastal Shandong between 1990 and 2023. The proportion of primary core areas (Level 1) dropped sharply from 82.44 to 30.28% of total arable land, suggesting marked degradation of the agricultural landscape. (2) An assessment of landscape pattern metrics reveals increasing fragmentation of cultivated land. In contrast, the western regions have retained more intact agricultural landscapes, marked by a higher share of cultivated land, more cohesive patch patterns, and stronger spatial aggregation. (3) Bivariate spatial autocorrelation (mean Moran’s I = 0.6516) and Pearson correlation (mean r = 0.9225) both point to a strong positive association between the Aggregation Index (AI) and the Percentage of Landscape (PLAND). Among the six cluster types, Clusters C, D, and F—characterized by large area shares, high aggregation, and strong connectivity—represent the most suitable regions for agriculture. Although the proportions of Clusters C and D declined (from 26.5 to 3.7% and 36.8 to 25.7%, respectively), farmland protection policies contributed to a notable rise in Cluster F, from 17.6 to 27.9% over the study period. (4) The influence of key drivers varies across different landscape metrics. Slope gradient shows the greatest explanatory power for the Perimeter-Area Fractal Dimension (PAFRAC), Patch Density (PD), and AI. Meanwhile, NDVI, nighttime light index, and GDP emerge as primary drivers for Connectance Index (CONNECT), Patch Cohesion Index (COHESION), and PLAND respectively, with regression coefficients ranging [0.43, 0.76], [−0.99, −0.67], and [−0.98, −0.12].

Land use conflicts (LUCs) have become a significant global issue. Accurately identifying potential LUCs is crucial for mediating these conflicts, optimizing land use structure, and enhancing land use function. The necessary conditions of LUCs are land use multi-suitability (LUMS), land resource scarcity (LRS), and diversity of demands (DD). However, few studies have approached LUC identification from these three dimensions simultaneously. In addition, when assessing the diversity of demand, only human needs are considered and wildlife needs are ignored. In order to address this gap in the research, this paper constructs a novel framework for LUC identification and proposes an induction-oriented governance path. LUMS was evaluated from three aspects: construction suitability, cultivation suitability, and ecological suitability. LRS is measured from three dimensions: construction land, cultivated land, and ecological land scarcity. The DD is expanded into human and wildlife demand diversity. By analyzing the combination of LUMS, LRS, and DD, LUCs are classified using the potential LUC identification Rubik’s cube model, and corresponding governance paths are suggested. In Shandong Province, potential LUCs are relatively high, with strong, medium, and weak conflicts accounting for 27.39%, 57.10%, and 13.06%, respectively. Potential strong conflicts are mainly distributed in the metropolitan suburbs and in the western plain of Shandong Province. Cultivated land is the main potential land use conflict space. The new framework of LUC identification proposed in this paper can effectively identify potential LUCs. Our research provides scientific reference for sustainable land use.