Sparse angle CT is an effective method to reduce X-ray radiation dose in clinical CT imaging. However, due to the incomplete projection caused by sparse sampling, the image reconstruction contains obvious fringe artifacts. In order to solve this problem, this paper proposes a sparse angle CT image reconstruction network based on iterative optimization deployment, IADR-Net, which adopts a unique dual-channel parallel architecture design, and includes two core components: Iterative reconstruction sub-network and global-local attention network (GLONA) detail recovery sub-network. Among them, the iterative reconstruction sub-network is based on the framework of fast iterative soft threshold algorithm, and realizes projection-to-image reconstruction through learnable nonlinear transformation and adaptive thresholding. The GLONA sub-network adopts a double-branch structure with parallel local and global features, and effectively maintains the image details through the self-adjusting fusion module. The two sub-networks work together to focus on artifact removal based on iterative expansion and detail enhancement based on attention mechanism, respectively, and finally output high-quality CT images through feature fusion. Experimental results on the Mayo dataset show that the proposed method has better performance than several representative algorithms in terms of artifact suppression and structure preservation, and provides an effective solution for clinical sparse angle CT imaging.