Land use/land cover represents the interactive and comprehensive influences between human activities and natural conditions, leading to potential conflicts among natural and human-related issues as well as among stakeholders. This study introduced economic standards for farmers. A hybrid approach (CA-ABM) of cellular automaton (CA) and an agent-based model (ABM) was developed to effectively deal with social and land use synergic issues to examine human–environment interactions and projections of land use conversions for a humid basin in south China. Natural attributes and socioeconomic data were used to analyze land use/land cover and its drivers of change. The major modules of the CA-ABM are initialization, migration, assets, land suitability, and land-use change decisions. Empirical estimates of the factors influencing the urban land use conversion probability were captured using parameters based on a spatial logistic regression (SLR) model. Simultaneously, multicriteria evaluation (MCE) and Markov models were introduced to obtain empirical estimates of the factors affecting the probability of ecological land conversion. An agent-based CA-SLR-MCE-Markov (ABCSMM) land-use conversion model was proposed to explore the impacts of policies on land-use conversion. This model can reproduce observed land-use patterns and provide links for forest transition and urban expansion to land-use decisions and ecosystem services. The results demonstrated land-use simulations under multi-policy scenarios, revealing the usefulness of the model for normative research on land-use management.