Objectives This study addresses the limitations of existing deep learning-based ship cross-section topology optimization methods, which are restricted to single-section ship structures. A fast topology optimization method for general ship cross-sections is proposed.

Methods The proposed method employs automated parametric modeling and computation techniques to construct a large-scale, structurally diverse dataset of ship cross-section static analysis and topology optimization results. This dataset enables deep supervised learning to train the neural network to rapidly generate reasonable topology optimization configurations for various ship cross-sections. Furthermore, to address the challenge of directly applying neural network predictions in engineering analysis, an algorithm was developed to automatically reconstruct finite element models from the binarized density tensor output by the neural network, thus overcoming the limitations of element removal methods and ensuring mechanical consistency between network predictions and traditional iterative calculation results.

Results Experimental results demonstrate that applying the proposed method to predict the topological configuration of various ship cross-sections reduces computation time by two orders of magnitude, with an average prediction accuracy exceeding 90%. Sampling inspection results indicate that the finite element models reconstructed based on the network predictions avoid stress concentration, with less than 3% deviation in mechanical performance compared to traditional iterative calculations, further verifying the reliability of the proposed method.

Conclusions The proposed method provides a general solution for the rapid topology optimization of ship cross-sections, reduces ship design costs, and possesses significant engineering value.