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Aiming at speeding up the process of topology optimization and generating a novel high-quality topology configuration, a topology optimization design with lightweight structure based on deep learning is investigated. First, Independent Continuous Mapping (ICM) method is employed to establish the original configurations including the intermediate configurations and the corresponding final configurations, and the original configurations are padded and merged to form the dataset required by the deep learning network. Then the high-dimensional mapping relationship between the intermediate configuration and the final configuration is created by using Pix2pix neural network (Pix2pix NN), which transforms the topology optimization into image-to-image translation problem. Finally, the acceleration algorithm is utilized to accelerate the iteration process by the pre-trained network. Numerical examples show that the coupling method is feasible and efficient in topology optimization design. The method provides a new solution for topology optimization design to shorten the iteration process and broaden the application of ICM method.

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