Identifying high-quality arable land is essential for effective arable land protection. In the assessment of arable land, where each sampling unit of arable land is recorded in a tabular format, current approaches primarily utilize decision tree ensembles and deep learning (DL) techniques. However, these methods are easily suffered from the data imbalance problem, a common issue in real-world arable land assessment scenarios that can lead to sub-optimal outcomes. Furthermore, traditional methods tend to treat each land grid as an independent sample, overlooking the significant spatial and topological interactions among adjacent areas. To overcome these challenges, we introduce SAFE, a Spatial-Aware Framework for arable land quality Evaluation that integrates convolutional neural network (CNN) and graph neural network (GNN) components into a deep learning-based architecture. This framework is designed to discern spatial local features and capture topological relationships within tabular data. Additionally, we incorporate a self-supervised regularization technique utilizing contrastive learning into the training objective of the architecture. This approach enhances feature embedding refinement and mitigates the effects of data imbalance. Experimental result demonstrates the distinct advantages of SAFE on a real-world dataset across multiple counties.