Magnetic resonance imaging （MRI） plays a crucial role in medical diagnosis， but prolonged scanning times can cause patients discomfort and motion artifacts. Parallel imaging techniques and compressed sensing theory indicate that undersampling k-space data can enhance the scanning speed， where parallel MRI accelerates the imaging process by utilizing multiple receiving coils to simultaneously acquire data from multiple channels. Leveraging its powerful feature extraction and pattern recognition capabilities， deep learning demonstrates great potential in undersampled MRI reconstruction. To overcome the limitations of existing technologies （e.g.， the need for automatic calibration signals， reconstruction instability）， this paper proposes an innovative reconstruction method aimed at efficiently and accurately reconstructing high-quality parallel MRI images from undersampled k-space data. The core framework of this method is a deep sparse network that unfolds the iterative process of the iterative shrinkage-thresholding algorithm （ISTA） for solving sparse models into a series of trainable layers within a deep neural network framework. Additionally， this paper introduces an adaptive preprocessing module based on multi-scale feature fusion， which further enhances the sparse representation capability of the network by integrating standard convolutions with heterogeneous convolutional kernels. Experimental results demonstrate that， compared to other advanced methods， the proposed method exhibits superior reconstruction performance across multiple datasets， including higher peak signal-to-noise ratio （PSNR） and structural similarity （SSIM）， as well as lower high-frequency error norms.