Image denoising, a fundamental task in computer vision, relies on leveraging effective prior knowledge to restore detailed information corrupted by noise. This study proposes a feature spatial contextual Transformer for image denoising FSCformer to address the performance bottlenecks of traditional convolutional neural network (CNN) caused by fixed weights and the limitation of local receptive fields, and the high computational complexity of Transformer for global modeling. In this method, an efficient receptive field module is introduced to dynamically capture multi-scale contextual information, enhancing spatial perception while significantly reducing computational overhead. Furthermore, a convolutional attention module is adopted to integrate local feature extraction with global dependency modeling, improving the robustness of the model especially in complex scenes. Additionally, a cross-feature fusion mechanism is proposed to preserve image details by promoting the fine-grained interaction of multi-scale features. Extensive experiments demonstrate that the proposed method achieves a favorable trade-off between denoising accuracy and computational efficiency, outperforming several existing image denoising models across multiple benchmark datasets.