Light-sheet fluorescence microscopy imaging systems are extensively used for imaging large-volume biological samples. However, as the field of view of the optical system expands, imaging will exhibit spatially uneven degradation throughout the entire field of view. Conventional model-driven and deep learning approaches exhibit spatial invariance, making it challenging to directly address this degradation. A position-dependent model-driven deconvolution network is developed by introducing positional information into the model-driven deconvolution network, which is achieved by randomly selecting training image pairs with different degradation patterns during training and using block-based reconstruction techniques during image restoration. The experimental results reveal that the network facilitates rapid deconvolution of large field-of-view optical images, thereby considerably enhancing image processing efficiency, image quality, and the uniformity of image quality within the field of view.