Functional Magnetic Resonance Imaging (fMRI) provides crucial insights into brain activity but presents challenges due to its high-dimensional, dynamic, and noisy nature. Traditional graph-based approaches for fMRI analysis often rely on predefined correlation structures, which may not accurately reflect the true underlying functional connectivity. To address this limitation, we propose a graph learning framework that dynamically constructs brain graphs and leverages Spline Convolutional Neural Networks (SplineCNN) for localized spatial feature extraction. Our model introduces a Learner Graph module, which infers graph structures in a data-driven manner, mitigating the reliance on predefined connectivity measures. The SplineCNN and Multi-Graph Convolution modules capture fine-grained spatial dependencies, offering improved adaptability to the heterogeneous nature of fMRI data. Additionally, we incorporate contrastive learning to align learned representations with domain-specific priors to improve generalization. Experimental results demonstrate that our approach outperforms traditional correlation-based methods in neurological disorder classification. The proposed framework provides a principled, adaptive solution for learning graph representations from fMRI, enhancing generalizability and robustness in brain network analysis.