Accurate Identification of High-Potential Reserved Cultivated Land Resources: A Convolutional Neural Network-Based Intelligent Selection Framework Verified in Qinghai Province on the Qinghai–Tibet Plateau, China

The sustainable use of farmland depends on the precise identification of promising reserved cultivated land resources, particularly in regions with fragmented spatial patterns and complex environmental conditions. Traditional evaluation methods often rely on limited indicators and neglect patch morphology, leading to restricted accuracy and applicability. To address this issue, an innovative intelligent-selection framework is proposed that integrates multi-source data evaluation with patch-morphology verification and employs convolutional neural networks (CNNs), applied in Qinghai Province, China. The framework combines one-dimensional and two-dimensional CNN models, incorporating 11 key indicators—including slope, irrigation conditions, and contiguity—together with patch morphology to predict development priority. Results show that the two models achieve predictive accuracies of 98.48% and 91.95%, respectively, outperforming the traditional Analytic Hierarchy Process (AHP) and effectively filtering out irregular patches unsuitable for cultivation. Further SHAP analysis and ablation experiments reveal the contributions of individual indicators, with slope identified as the dominant factor in prioritization. Overall, the study demonstrates that integrating multi-source data evaluation with patch-morphology verification within a machine-learning framework significantly enhances prioritization accuracy. The proposed framework provides a transferable, evidence-based pathway for the graded utilization of reserved cultivated land resources and the reinforcement of farmland security strategies.